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• Part I: Knowledge Engineering and Semantic Web.- A Three-stage Consensus-based Method for Collective Knowledge Determination.- Fuzzy Ontology Modeling by Utilizing Fuzzy Set and Fuzzy Description Logic.- An Approach for Recommending Group Experts on Question and Answering Sites.- A method for uncertainty elicitation of experts using belief function.- Storing Hypergraph-based Data Models in Non-hypergraph Data Storage.- Part II: Natural Language Processing and Text Mining.- A Fuzzy Logic Approach to Predict the Popularity of a Presidential Candidate.- DNA Sequences Representation Derived from Discrete Wavelet Transformation for Text Similarity Recognition.- Tweet integration by finding the shortest paths on a word graph.- Event detection in Twitter: Methodological Evaluation and Structural Analysis of the Bibliometric Data.- Combination of inner approach and context-based approach for extracting feature of medical record data.- A Novel Method to Predict Type for DBpedia Entity.- Context-Based Personalized Predictors of the Length of Written Responses to Open-ended Questions of Elementary School Students.- Part III: Machine Learning and Data Mining.- Robust Scale-Invariant Normalization and Similarity Measurement for Time Series Data.
• (source: Nielsen Book Data)

This book offers a unique blend of reports on both theoretical models and their applications in the area of Intelligent Information and Database Systems. The reports cover a broad range of research topics, including advanced learning techniques, knowledge engineering, Natural Language Processing (NLP), decision support systems, Internet of things (IoT), computer vision, and tools and techniques for Intelligent Information Systems. They are extended versions of papers presented at the ACIIDS 2018 conference (10th Asian Conference on Intelligent Information and Database Systems), which was held in Dong Hoi City, Vietnam on 19-21 March 2018. What all researchers and students of computer science need is a state-of-the-art report on the latest trends in their respective areas of interest. Over the years, researchers have proposed increasingly complex theoretical models, which provide the theoretical basis for numerous applications. The applications, in turn, have a profound influence on virtually every aspect of human activities, while also allowing us to validate the underlying theoretical concepts.
(source: Nielsen Book Data)

• Data in the 21st Century.- The Understanding of Dependent Structure and Co-Movement of World Stock Exchanges Under the Economic Cycle.- Macro-Econometric Forecasting for During Periods of Economic Cycle Using Bayesian Extreme Value Optimization Algorithm.- Generalize Weighted in Interval Data for Fitting a Vector Autoregressive Model.- Asymmetric Effect with Quantile Regression for Interval-valued Variables.- Emissions, Trade Openness, Urbanisation, and Income in Thailand: An Empirical Analysis.- Does Forecasting Benefit from Mixed-Frequency Data Sampling Model: The Evidence from Forecasting GDP Growth Using Financial Factor in Thailand.- How Better Are Predictive Models: Analysis on the Practically Important Example of Robust Interval Uncertainty.
• (source: Nielsen Book Data)

This book presents recent research on predictive econometrics and big data. Gathering edited papers presented at the 11th International Conference of the Thailand Econometric Society (TES2018), held in Chiang Mai, Thailand, on January 10-12, 2018, its main focus is on predictive techniques - which directly aim at predicting economic phenomena; and big data techniques - which enable us to handle the enormous amounts of data generated by modern computers in a reasonable time. The book also discusses the applications of more traditional statistical techniques to econometric problems. Econometrics is a branch of economics that employs mathematical (especially statistical) methods to analyze economic systems, to forecast economic and financial dynamics, and to develop strategies for achieving desirable economic performance. It is therefore important to develop data processing techniques that explicitly focus on prediction. The more data we have, the better our predictions will be. As such, these techniques are essential to our ability to process huge amounts of available data.
(source: Nielsen Book Data)

• Introduction to Swarm Search.- Cooperative "Swarm Cleaning" of Stationary Domains.- Swarm Search of Expanding Regions in Grids: Lower Bounds.- Swarm Search of Expanding Regions in Grids: Upper Bounds.- The Search Complexity of Collaborative Swarms Expanding Z2 Grid Regions.
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This book offers a comprehensive analysis of the theory and tools needed for the development of an efficient and robust infrastructure for the design of collaborative patrolling unmanned aerial vehicle (UAV) swarms, focusing on its applications for tactical intelligence drones. It discusses frameworks for robustly and near-optimally analyzing flocks of semi-autonomous vehicles designed to efficiently perform the ongoing dynamic patrolling and scanning of pre-defined "search regions". It discusses the theoretical limitations of such systems, as well as the trade-offs between the systems' various economic and operational parameters. Current UAV systems rely mainly on human operators for the design and adaptation of drones' flying routes. However, recent technological advances have introduced new systems, comprised of a small number of self-organizing vehicles, manually guided at the swarm level by a human operator. With the growing complexity of such man-supervised architectures, it is becoming increasingly harder to guarantee a pre-defined level of performance. The use of large scale swarms of UAVs as a combat and reconnaissance platform therefore necessitates the development of an efficient optimization mechanism of their utilization, specifically in the design and maintenance of their patrolling routes. The book is intended for researchers and engineers in the fields of swarms systems and autonomous drones. .
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• Modelling of Underwater Robots.- Dynamic Control of 6-DOF AUVs.- Fault Detection/Tolerance Strategies for AUVs and ROVs.- Experiments of Dynamic Control of a 6-DOF AUV.- Kinematic Control of UVMSs.- Dynamic Control of UVMSs.- Interaction Control of UVMSs.- Dynamic Control of 6-DOF AUVs.- Fault Detection/Tolerance Strategies for AUVs and ROVs.- Experiments of Dynamic Control of a 6-DOF AUV.- Kinematic Control of UVMSs.- Dynamic Control of UVMSs.- Interaction Control of UVMSs.
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A classic in underwater robotics. One of the first volumes in the "Springer Tracts in Advanced Robotics" series, it has been a bestseller through the previous three editions. Fifteen years after the publication of the first edition, the fourth edition comes to print. The book addresses the main control aspects in underwater manipulation tasks. With respect to the third edition, it has been revised, extended and some concepts better clustered. The mathematical model with significant impact on the control strategy is discussed. The problem of controlling a 6-degrees-of-freedoms autonomous underwater vehicle is investigated and a survey of fault detection/tolerant strategies for unmanned underwater vehicles is provided. Inverse kinematics, dynamic and interaction control for underwater vehicle-manipulator systems are then discussed. The code used to generate most of the numerical simulations is made available and briefly discussed.
(source: Nielsen Book Data)

• Introduction.- Part 1:Background and Methodology.- Discrete Consensus Achievement in Artificial Systems.- Modular Design of Strategies for the Best-of-n Problem.- Part 2:Mathematical Modeling and Analysis.- Indirect Modulation of Majority-Based Decisions.- Direct Modulation of Voter-Based Decisions.- Direct Modulation of Majority-Based Decisions.- Part 3:Robot Experiments.- A Robot Experiment in Site Selection.- A Robot Experiment in Collective Perception.- Part 4:Discussion and Annexes.- Conclusions.- Background on Markov Chains.
• (source: Nielsen Book Data)

This book focuses on the design and analysis of collective decision-making strategies for the best-of-n problem. After providing a formalization of the structure of the best-of-n problem supported by a comprehensive survey of the swarm robotics literature, it introduces the functioning of a collective decision-making strategy and identifies a set of mechanisms that are essential for a strategy to solve the best-of-n problem. The best-of-n problem is an abstraction that captures the frequent requirement of a robot swarm to choose one option from of a finite set when optimizing benefits and costs. The book leverages the identification of these mechanisms to develop a modular and model-driven methodology to design collective decision-making strategies and to analyze their performance at different level of abstractions. Lastly, the author provides a series of case studies in which the proposed methodology is used to design different strategies, using robot experiments to show how the designed strategies can be ported to different application scenarios.
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• Preface.- Acknowledgements.- Chapte
• r1: Introduction.- Chapte
• r2: Selected topics in fuzzy systems designing.- Chapte
• r3: Introduction to fuzzy system interpretability.- Chapte
• r4: Improving fuzzy systems interpretability by appropriate selection of their structure.- Chapte
• r5: Interpretability of fuzzy systems designed in the process of gradient learning.- Chapte
• r6: Interpretability of fuzzy systems designed in the process of evolutionary learning.- Chapte
• r7: Case study: interpretability of fuzzy systems applied to nonlinear modelling and control.- Chapte
• r8: Case study: interpretability of fuzzy systems applied to identity verification.- Chapte
• r9: Concluding remarks and future perspectives.- Index.
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This book shows that the term "interpretability" goes far beyond the concept of readability of a fuzzy set and fuzzy rules. It focuses on novel and precise operators of aggregation, inference, and defuzzification leading to flexible Mamdani-type and logical-type systems that can achieve the required accuracy using a less complex rule base. The individual chapters describe various aspects of interpretability, including appropriate selection of the structure of a fuzzy system, focusing on improving the interpretability of fuzzy systems designed using both gradient-learning and evolutionary algorithms. It also demonstrates how to eliminate various system components, such as inputs, rules and fuzzy sets, whose reduction does not adversely affect system accuracy. It illustrates the performance of the developed algorithms and methods with commonly used benchmarks. The book provides valuable tools for possible applications in many fields including expert systems, automatic control and robotics.
(source: Nielsen Book Data)

• Chapter 1 Introduction.-
• Chapter 2 Some Theoretical Background.-
• Chapter 3 Spatial Grasp Model.-
• Chapter 4 SGL Detailed Specification.-
• Chapter 5 Main Spatial Mechanisms in SGL.-
• Chapter 6 SGL Networked Interpreter.-
• Chapter 7 Creation, Activation and Management of a Distributed World.-
• Chapter 8 Parallel and Distributed Network Operations.-
• Chapter 9 Solving Social Problems.-
• Chapter 10 Automated Command and Control.-
• Chapter 11 Collective Robotics.-
• Chapter 12 Conclusions.
• (source: Nielsen Book Data)

The book describes a novel ideology and supporting information technology for integral management of both civil and defence-orientated large, distributed dynamic systems. The approach is based on a high-level Spatial Grasp Language, SGL, expressing solutions in physical, virtual, executive and combined environments in the form of active self-evolving and self-propagating patterns spatially matching the systems to be created, modified and controlled. The communicating interpreters of SGL can be installed in key system points, which may be in large numbers (up to millions and billions) and represent equipped humans, robots, laptops, smartphones, smart sensors, etc. Operating under gestalt-inspired scenarios in SGL initially injected from any points, these systems can be effectively converted into goal-driven spatial machines (rather than computers as dealing with physical matter too) capable of responding to numerous challenges caused by growing world dynamics in the 21st century. Including numerous practical examples, the book is a valuable resource for system managers and programmers.
(source: Nielsen Book Data)

• Machine generated contents note: ch. 1 Introduction
• 1.1. Linked Data
• 1.2. Entity Name System
• 1.3. Research Question and Thesis
• 1.4. Dissertation
• 1.5. Contributions
• ch. 2 Background
• 2.1. Structured Data Models
• 2.1.1. Resource Description Framework (RDF)
• 2.1.2. Relational Database (RDB) Model
• 2.1.3. Serializing RDF Data
• 2.2. Instance Matching
• 2.2.1. Blocking Step
• 2.2.2. Similarity Step
• 2.2.3. Evaluating Instance Matching
• 2.3. Heterogeneity
• 2.3.1. Type Heterogeneity
• 2.3.2. Property Heterogeneity
• 2.3.3. Extending the Two-Step Workflow
• 2.4. Scalability
• 2.4.1. Motivation
• 2.4.2. Implementation
• ch. 3 Related Work
• 3.1. Existing Domain-Independent Systems
• 3.1.1. Systems Addressing Automation
• 3.1.2. Systems Addressing Heterogeneity
• 3.1.3. Systems Addressing Scalability
• 3.1.4. Other Systems
• 3.2. Discussion
• 3.2.1. Automation vs. Scalability
• 3.2.2. Issues of Structural Heterogeneity
• 3.3.3. Issues of Unsupervised Blocking
• ch. 4 Type Alignment
• 4.1. Motivating Example and Preliminaries: A Review
• 4.2. Applications of Type Alignment
• 4.3. Approach
• 4.3.1. Possible Strategy Implementations
• 4.4. Evaluations
• 4.4.1. Test Cases
• 4.4.2. Metrics and Methodology
• 4.4.3. Results and Discussion
• ch. 5 Training Set Generation
• 5.1. Intuition
• 5.2. Approach
• 5.3. Evaluations
• 5.3.1. Test Suite
• 5.3.2. Metrics
• 5.3.3. Setup
• 5.3.4. Results and Discussion
• ch. 6 Property Alignment
• 6.1. Approach
• 6.2. Evaluations
• 6.2.1. Setup
• 6.2.2. Results and Discussion
• ch. 7 Blocking and Classification
• 7.1. Approach
• 7.1.1. Feature Generator
• 7.1.2. Learning Procedures
• 7.2. Evaluations
• 7.2.1. Blocking
• 7.2.2. Similarity (non-iterative run)
• 7.2.3. Similarity (iterative run)
• ch. 8 Scalability
• 8.1. Summary of Algorithms
• 8.2. Motivation and Use-Cases
• 8.3. MapReduce Implementations
• 8.3.1. Type Alignment
• 8.3.2. Training Set Generator
• 8.3.3. Property Alignment and Learning Procedures
• 8.3.4. Blocking and Similarity
• ch. 9 Conclusion
• 9.1. Summary
• 9.2. Future Work
• 9.2.1. Linked Data Quality
• 9.2.2. Schema-Free Approaches
• 9.2.3. Transfer Learning.

• Foundations of Intelligent Computing
• Intelligent Techniques in Data Mining
• Multi-Agent Based Technologies
• Intelligent Computing in Decision Support Systems
• Intelligent Text and Data Retrieval: Towards a Better Representation of Users Intentions.

• 4.1 Function ξ in Fermat's Equation4.2 Generalization to Arbitrary Sequences; 5 Conclusions; References; Part IIIntelligent Techniques inData Mining; Forecasting of Short Time Series with Intelligent Computing; 1 Introduction; 2 Recent Trends in Forecasting with Intelligent Computing; 2.1 Fuzzy Time Series; 2.2 Fuzzification of Model Parameters; 2.3 Fuzzy Rule Based Systems; 2.4 Hybrid Systems; 3 Proposed Approach Using Linguistic Summaries; 4 Numerical Results; 4.1 Illustrative Example; 4.2 Comparative Analysis; 5 Conclusion; References; An Improved Adaptive Self-Organizing Map

• On the Identification of α-Asynchronous Cellular Automata in the Case of Partial Observations with Spatially Separated Gaps1 Introduction; 2 Preliminaries; 3 α-Asynchronous CAs; 4 Identification Problem; 5 Identification Algorithm; 5.1 Complete Observations; 5.2 Gap Filling Procedure; 6 Experimental Results; 7 Summary; References; k-Arithmetic Sequences---Theory and Applications; 1 Introduction; 2 k-Arithmetic Sequences; 2.1 Basic Concepts; 2.2 Characterizing Sequence; 3 Another View on Polynomials; 3.1 Characterizing Sequence and Polynomial Coefficients; 3.2 Basis Numbers; 4 Applications

This volume presents recent research, challenging problems and solutions in Intelligent Systems? covering the following disciplines: artificial and computational intelligence, fuzzy logic and other non-classic logics, intelligent database systems, information retrieval, information fusion, intelligent search (engines), data mining, cluster analysis, unsupervised learning, machine learning, intelligent data analysis, (group) decision support systems, intelligent agents and multi-agent systems, knowledge-based systems, imprecision and uncertainty handling, electronic commerce, distributed systems, etc. The book defines a common ground for sometimes seemingly disparate problems and addresses them by using the paradigm of broadly perceived intelligent systems. It presents a broad panorama of a multitude of theoretical and practical problems which have been successfully dealt with using the paradigm of intelligent computing.

• Neural Networks
• . Neural Networks with Feedback and Self-organization Introduction
• Fuzzy Inference Systems and Fuzzy Neural Networks
• Application of Fuzzy Logic Systems and Fuzzy Neural Networks in Forecasting Problems in Macroeconomics and Finance
• Fuzzy Neural Networks in Classification Problems
• Inductive Modeling Method (gmdh) in Problems of Intellectual Data Analysis and Forecasting
• The Cluster Analysis in Intellectual Systems
• Genetic Algorithms and Evolutionary Programing
• Problem of Fuzzy Portfolio optimization Under Uncertainty And Its Solution With Application of Computational Intelligence Methods.

This monograph is dedicated to the systematic presentation of main trends, technologies and methods of computational intelligence (CI). The book pays big attention to novel important CI technology- fuzzy logic (FL) systems and fuzzy neural networks (FNN). Different FNN including new class of FNN- cascade neo-fuzzy neural networks are considered and their training algorithms are described and analyzed. The applications of FNN to the forecast in macroeconomics and at stock markets are examined. The book presents the problem of portfolio optimization under uncertainty, the novel theory of fuzzy portfolio optimization free of drawbacks of classical model of Markovitz as well as an application for portfolios optimization at Ukrainian, Russian and American stock exchanges. The book also presents the problem of corporations bankruptcy risk forecasting under incomplete and fuzzy information, as well as new methods based on fuzzy sets theory and fuzzy neural networks and results of their application for bankruptcy risk forecasting are presented and compared with Altman method. This monograph also focuses on an inductive modeling method of self-organization - the so-called Group Method of Data Handling (GMDH) which enables to construct the structure of forecasting models almost automatically. The results of experimental investigations of GMDH for forecasting at stock exchanges are presented. The final chapters are devoted to theory and applications of evolutionary modeling (EM) and genetic algorithms. The distinguishing feature of this monograph is a great number of practical examples of CI technologies and methods application for solution of real problems in technology, economy and financial sphere, in particular forecasting, classification, pattern recognition, portfolio optimization, bankruptcy risk prediction under uncertainty which were developed by authors and published in this book for the first time. All CI methods and algorithms are presented from the general system approach and analysis of their properties, advantages and drawbacks that enables practitioners to choose the most adequate method for their own problems solution. .

• Aerial Vehicles progress On Pico Air Vehicles.- Perception and Mapping.- Planning.- Systems and Integration.- Control.
• (source: Nielsen Book Data)

This volume presents a collection of papers presented at the 15th International Symposium of Robotic Research (ISRR). ISRR is the biennial meeting of the International Foundation of Robotic Research (IFRR) and its 15th edition took place in Flagstaff, Arizona on December 9 to December 12, 2011. As for the previous symposia, ISRR 2011 followed up on the successful concept of a mixture of invited contributions and open submissions. Therefore approximately half of the 37 contributions were invited contributions from outstanding researchers selected by the IFRR officers and the program committee, and the other half were chosen among the open submissions after peer review. This selection process resulted in a truly excellent technical program which featured some of the very best of robotic research. The program was organized around oral presentation in a single-track format and included for the first time a small number of interactive presentations. The symposium contributions contained in this volume report on a variety of new robotics research results covering a broad spectrum including perception, manipulation, grasping, vehicles and design, navigation, control and integration, estimation and SLAM. .
(source: Nielsen Book Data)

• Foreword vii Preface ix Acknowledgement xi
• Chapter 1: Introduction 1-20 1.1 Computational Intelligence 1 1.2 Paradigms of Computational Intelligence 2 1.3 Synergies of Computational Intelligence Techniques 11 1.4 Applications of Computational Intelligence 13 1.5 Grand Challenges of Computational Intelligence 14 1.6 Overview of the Book 14 1.7 Matlab Basics 16 1.8 Bibliography 17
• Chapter 2: Fuzzy Logic 21-78 2.1 Introduction 21 2.2 Fuzzy Logic 23 2.3 Fuzzy Sets 24 2.4 Membership Functions 25 2.5 Features of MFs 30 2.6 Operations on Fuzzy sets 32 2.7 Linguistic Variables 39 2.8 Linguistic Hedges 42 2.9 Fuzzy Relations 45 2.10 Fuzzy If-Then Rules 48 2.11 Fuzzification 52 2.12 Defuzzification 54 2.13 Inference Mechanism 59 2.13.1 Mamdani Fuzzy Inference 60 2.13.2 Sugeno Fuzzy Inference 61 2.13.3 Tsukamoto Fuzzy Inference 65 2.14 Worked out Examples 67 2.15 Matlab Programs 76 2.16 Bibliography 77
• Chapter 3: Fuzzy Systems and Applications 79-128 3.1 Introduction 79 3.2 Fuzzy System 80 3.3 Fuzzy Modelling 81 3.3.1 Structure Identification 82 3.3.2 Parameter Identification 85 3.3.3 Construction of parameterised Membership Functions 86 3.4 Fuzzy Control 92 3.4.1 Fuzzification 93 3.4.2 Inference Mechanism 93 3.4.3 Rule-base 98 3.4.4 Defuzzification 100 3.5 Design of Fuzzy Controller 101 3.5.1 Input-output Selection 102 3.5.2 Choice of Membership Functions 102 3.5.3 Creation of Rule-base 103 3.5.4 Types of Fuzzy Controller 104 3.6 Modular Fuzzy Controller .121 3.7 Matlab Programs 124 3.8 Bibliography 125
• Chapter 4: Neural Networks 129-201 4.1 Introduction 129 4.2 Artificial Neuron Model 130 4.3 Activation Functions 132 4.4 Network Architecture 134 4.4.1 Feedforward Networks 134 4.4.1.1 Multilayer Perceptron (MLP) Networks 136 4.4.1.2 Radial Basis Function (RBF) Networks 138 4.4.1.3 General Regression Neural Networks 142 4.4.1.4 Probabilistic Neural Network 146 4.4.1.5 Belief Network 149 4.4.1.6 Hamming Network 150 4.4.1.7 Stochastic Networks 153 4.5 Learning in Neural Networks 153 4.5.1 Supervised learning 154 4.5.1.1 Widro-Hoff Learning Algorithm 155 4.5.1.2 Gradient Descent Rule 4.5.1.3 Generalised Delta Learning Rule 162 4.5.1.4 Backpropagation Learning Algorithm 165 4.5.1.5 Cohen-Grossberg Learning Rule 171 4.5.1.6 Adaptive Conjugate Gradient Model of Adeli and Hung 173 4.5.2 Unsupervised Learning 173 4.5.2.1 Hebbian Learning Rule 174 4.5.2.2 Kohonen Learning 178 4.6 Recurrent Neural Networks 187 4.6.1 Elman Networks 189 4.6.2 Jordan Networks 192 4.6.3 Hopfield Networks 194 4.7 Matlab Programs 198 4.8 Bibliography 198
• Chapter 5: Neural Systems 202-232 5.1 Introduction 200 5.2 System Identification and Control 201 5.2.1 System Description 201 5.2.2 System Identification 202 5.2.3 System Control ..203 5.3 Neural Networks for Control 205 5.3.1 System Identification 206 5.3.2 Neural Networks for Control Design 208 5.3.2.1 NN-based direct (or specialised learning) control 209 5.3.2.2 NN-based indirect control .210 5.3.2.3 Backpropagation-through time control 211 5.3.2.4 NN-based direct inverse control 212 5.3.2.5 Model Predictive Control 214 5.3.2.6 NN-based Adaptive Control 216 5.3.2.7 NARMA-L2 (Feedback Linearization) Control 223 5.4 Matlab Programs 226 5.5 Bibliography 227
• Chapter 6: Evolutionary Computation 233-304 6.1 Introduction 233 6.2 Evolutionary Computing 234 6.3 Terminologies of Evolutionary Computing 235 6.3.1 Chromosome Representation 235 6.3.2 Encoding Scheme 236 6.3.3 Population 243 6.3.4 Evaluation (or Fitness) Functions 245 6.3.5 Fitness Scaling 246 6.4 Genetic Operators 247 6.4.1 Selection Operators 247 6.4.2 Crossover Operators 252 6.4.3 Mutation Operators 261 6.5 Performance Measure of EA 264 6.6 Evolutionary Algorithms 265 6.6.1 Evolutionary Programming 265 6.6.2 Evolution Strategies 271 6.6.3 Genetic Algorithms 277 6.6.4 Genetic Programming 283 6.6.5 Differential Evolution 294 6.6.6 Cultural Algorithm 299 6.7 Matlab Programs 300 6.8 Bibliography 301
• Chapter 7: Evolutionary Systems 305-340 7.1 Optimisation .305 7.2 Multi-objective Optimisation ..310 7.2.1 Vector Evaluated GA 315 7.2.2 Multi-objective GA 315 7.2.3 Niched Pareto GA .316 7.2.4 Non-dominated Sorting GA 316 7.2.5 Strength Pareto Evolutionary Algorithm 318 7.3 Co-evolution .319 7.3.1 Cooperative Co-evolution 324 7.3.2 Competitive Co-evolution .326 7.4 Parallel Evolutionary Algorithms 328 7.4.1 Global GA 329 7.4.2 Migration (or Island) Model GA 330 7.4.3 Diffusion GA .331 7.4.4 Hybrid Parallel GA 334 7.5 Bibliography .336
• Chapter 8: Evolutionary Fuzzy Systems 341-392 8.1 Introduction 341 8.2 Evolutionary Adaptive Fuzzy Systems 343 8.2.1 Evolutionary Tuning of Fuzzy Systems 345 8.2.2 Evolutionary Learning of Fuzzy Systems 361 8.3 Objective Functions and Evaluation 368 8.3.1 Objective Functions 368 8.3.2 Evaluation 370 8.4 Fuzzy Adaptive Evolutionary Algorithms 371 8.4.1 Fuzzy Logic based Control of EA Parameters 374 8.4.2 Fuzzy Logic based Genetic Operators of EA 387 8.5 Bibliography 388
• Chapter 9: Evolutionary Neural Systems 393-455 9.1 Introduction 393 9.2 Supportive Combinations 395 9.2.1 NN-EA Supportive Combination 395 9.2.2 EA-NN Supportive Combination 398 9.3 Collaborative Combinations 406 9.3.1 EA for NN Connection Weight Training 408 9.3.2 EA for NN Architectures 416 9.3.3 EA for NN Node Transfer Functions 430 9.3.4 EA for NN Weight, Architecture and Transfer Function Training 434 9.4 Amalgamated Combination 437 9.5 Competing Conventions 440 9.6 Bibliography 447
• Chapter 10: Neuro Fuzzy Systems 455-530 10.1 Introduction 455 10.2 Combination of Neural and Fuzzy Systems 458 10.3 Cooperative Neuro-Fuzzy Systems 459 10.3.1 Cooperative FS-NN Systems 460 10.3.2 Cooperative NN-FS Systems 461 10.4 Concurrent Neuro-Fuzzy Systems 470 10.5 Hybrid Neuro-Fuzzy Systems 471 10.5.1 Fuzzy Neural Networks with Mamdani-type Fuzzy Inference System 472 10.5.2 Fuzzy Neural Networks with Takagi-Sugeno-type Fuzzy Inference System 474 10.5.3 Fuzzy Neural Networks with Tsukamoto-type Fuzzy Inference System 476 10.5.4 Neural Network based Fuzzy System (Sigma-Pi Network) 480 10.5.5 Fuzzy-Neural System Architecture with Ellipsoid Input Space 484 10.5.6 Fuzzy Adaptive Learning Control Network (FALCON) 487 10.5.7 Approximate Reasoning based Intelligent Control (ARIC) 490 10.5.8 Generalised ARIC (GARIC) 495 10.5.9 Fuzzy Basis Function Networks (FBFN) 502 10.5.10 FUzzy Net (FUN) 505 10.5.11 Combination of Fuzzy Inference and Neural Network in Fuzzy Inference Software (FINEST) 507 10.5.12 Neuro-Fuzzy Controller (NEFCON) 510 10.5.13 Self-constructing Neural Fuzzy Inference Network (SONFIN) 512 10.6 Adaptive Neuro-Fuzzy System 515 10.6.1 Adaptive Neuro-Fuzzy Inference System (ANFIS) 516 10.6.2 Coactive Neuro-Fuzzy Inference System (CANFIS) 519 10.7 Fuzzy Neurons 523 10.8 Matlab Programs 526 10.9 Bibliography 527 Appendix531-606 Index.
• (source: Nielsen Book Data)

• Foreword xiii Preface xv Acknowledgements xix
• 1 Introduction to Computational Intelligence 1 1.1 Computational Intelligence 1 1.2 Paradigms of Computational Intelligence 2 1.3 Approaches to Computational Intelligence 3 1.4 Synergies of Computational Intelligence Techniques 11 1.5 Applications of Computational Intelligence 12 1.6 Grand Challenges of Computational Intelligence 13 1.7 Overview of the Book 13 1.8 MATLAB R - Basics 14 References 15
• 2 Introduction to Fuzzy Logic 19 2.1 Introduction 19 2.2 Fuzzy Logic 20 2.3 Fuzzy Sets 21 2.4 Membership Functions 22 2.5 Features of MFs 27 2.6 Operations on Fuzzy Sets 29 2.7 Linguistic Variables 33 2.8 Linguistic Hedges 35 2.9 Fuzzy Relations 37 2.10 Fuzzy If--Then Rules 39 2.11 Fuzzification 43 2.12 Defuzzification 44 2.13 Inference Mechanism 48 2.14 Worked Examples 54 2.15 MATLAB R - Programs 61 References 61
• 3 Fuzzy Systems and Applications 65 3.1 Introduction 65 3.2 Fuzzy System 66 3.3 Fuzzy Modelling 67 3.4 Fuzzy Control 75 3.5 Design of Fuzzy Controller 81 3.6 Modular Fuzzy Controller 97 3.7 MATLAB R - Programs 99 References 100
• 4 Neural Networks 103 4.1 Introduction 103 4.2 Artificial Neuron Model 106 4.3 Activation Functions 107 4.4 Network Architecture 108 4.5 Learning in Neural Networks 124 4.6 Recurrent Neural Networks 149 4.7 MATLAB R - Programs 155 References 156
• 5 Neural Systems and Applications 159 5.1 Introduction 159 5.2 System Identification and Control 160 5.3 Neural Networks for Control 163 5.4 MATLAB R - Programs 179 References 180
• 6 Evolutionary Computing 183 6.1 Introduction 183 6.2 Evolutionary Computing 183 6.3 Terminologies of Evolutionary Computing 185 6.4 Genetic Operators 194 6.5 Performance Measures of EA 208 6.6 Evolutionary Algorithms 209 6.7 MATLAB R - Programs 234 References 235
• 7 Evolutionary Systems 239 7.1 Introduction 239 7.2 Multi-objective Optimization 243 7.3 Co-evolution 250 7.4 Parallel Evolutionary Algorithm 256 References 262
• 8 Evolutionary Fuzzy Systems 265 8.1 Introduction 265 8.2 Evolutionary Adaptive Fuzzy Systems 267 8.3 Objective Functions and Evaluation 287 8.4 Fuzzy Adaptive Evolutionary Algorithms 290 References 303
• 9 Evolutionary Neural Networks 307 9.1 Introduction 307 9.2 Supportive Combinations 309 9.3 Collaborative Combinations 318 9.4 Amalgamated Combination 343 9.5 Competing Conventions 345 References 351
• 10 Neural Fuzzy Systems 357 10.1 Introduction 357 10.2 Combination of Neural and Fuzzy Systems 359 10.3 Cooperative Neuro-Fuzzy Systems 360 10.4 Concurrent Neuro-Fuzzy Systems 369 10.5 Hybrid Neuro-Fuzzy Systems 369 10.6 Adaptive Neuro-Fuzzy System 404 10.7 Fuzzy Neurons 409 10.8 MATLAB R - Programs 411 References 412 Appendix A: MATLAB R - Basics 415 Appendix B: MATLAB R - Programs for Fuzzy Logic 433 Appendix C: MATLAB R - Programs for Fuzzy Systems 443 Appendix D: MATLAB R - Programs for Neural Systems 461 Appendix E: MATLAB R - Programs for Neural Control Design 473 Appendix F: MATLAB R - Programs for Evolutionary Algorithms 489 Appendix G: MATLAB R - Programs for Neuro-Fuzzy Systems 497 Index 507.
• (source: Nielsen Book Data)

Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing presents an introduction to some of the cutting edge technological paradigms under the umbrella of computational intelligence. Computational intelligence schemes are investigated with the development of a suitable framework for fuzzy logic, neural networks and evolutionary computing, neuro-fuzzy systems, evolutionary-fuzzy systems and evolutionary neural systems. Applications to linear and non-linear systems are discussed with examples. Key features: Covers all the aspects of fuzzy, neural and evolutionary approaches with worked out examples, MATLAB(R) exercises and applications in each chapter Presents the synergies of technologies of computational intelligence such as evolutionary fuzzy neural fuzzy and evolutionary neural systems Considers real world problems in the domain of systems modelling, control and optimization Contains a foreword written by Lotfi Zadeh Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing is an ideal text for final year undergraduate, postgraduate and research students in electrical, control, computer, industrial and manufacturing engineering.
(source: Nielsen Book Data)

• The Real Problem of Consciousness
• Consciousness and Subjective Experience
• Perception and Qualia
• From Perception to Consciousness
• Emotions and Consciousness
• Inner Speech and Consciousness
• Qualia and Machine Consciousness
• Testing Consciousness
• Artificial Conscious Cognition
• Associative Information Processing
• Neural Realization of Associative Processing
• Designing a Cognitive Perception System
• Examples of Perception/Response Feedback Loops
• The Transition to Symbolic Processing
• Information Integration with Multiple Modules
• Emotional Significance of Percepts
• The Outline of the Haikonen Cognitive Architecture (HCA)
• Mind Reading Applications
• The Comparison of Some Cognitive Architectures
• Example: An Experimental Robot with the HCA
• Concluding Notes
• Consciousness Explained.
• (source: Nielsen Book Data)

Robots are becoming more human, but could they also become sentient and have human-like consciousness?What is consciousness, exactly?It is a fact that our thoughts and consciousness are based on the neural activity of the brain. It is also a fact that we do not perceive our brain activity as it really is - patterns of neural firings. Instead, we perceive our sensations and thoughts apparently as they are. What kind of condition would transform the neural activity into this kind of internal appearance? This is the basic problem of consciousness.The author proposes an explanation that also provides preconditions for true conscious cognition - the requirement of a direct perceptive system with inherent sub-symbolic and symbolic information processing. Associative neural information processing with distributed signal representations is introduced as a method that satisfies these requirements.Conscious robot cognition also calls for information integration and sensorimotor integration. This requirement is satisfied by the Haikonen Cognitive Architecture (HCA).This book demystifies both the enigmatic philosophical issues of consciousness and the practical engineering issues of conscious robots by presenting them in an easy-to-understand manner for the benefit of students, researchers, philosophers and engineers in the field.
(source: Nielsen Book Data)

• Introduction
• Foundation of Neuro-Physiology
• Neural Computing
• Mind Model
• Perception
• Visual Information Processing
• Audio Information Processing
• Language
• Learning
• Memory
• Thought
• Development of Intelligence
• Emotion
• Immune System
• Consciousness
• Symbolic Logic
• The Machine Proves
• Perspective.
• (source: Nielsen Book Data)

Intelligence Science is an interdisciplinary subject dedicated to joint research on basic theory and technology of intelligence by brain science, cognitive science, artificial intelligence and others. Brain science explores the essence of brain research on the principle and model of natural intelligence at the molecular, cell and behavior level. Cognitive science studies human mental activity, such as perception, learning, memory, thinking, consciousness etc. In order to implement machine intelligence, artificial intelligence attempts simulation, extension and expansion of human intelligence using artificial methodology and technology. Research scientists from the above three disciplines work together to explore new concepts, new theories, and methodologies.This book will introduce the concept and methodology of intelligence science systematically. The whole book is divided into 18 chapters altogether. It can be regarded as a textbook in courses of intelligence science, cognitive science, cognitive informatics etc. for senior and graduate students. It has important reference value for researchers engaged in fields such as intelligence science, brain science, cognitive science, neural science, artificial intelligence, psychology and so on.
(source: Nielsen Book Data)

• Preface
• Application of Artificial Intelligence in the Upstream Oil & Gas Industry
• Modeling & Optimization of the Effect of Laser Marking Parameters on Gloss of the Laser Marked Gold Using Artificial Intelligence Approaches
• AI Applications to Metal Stamping Die Design
• Structural Features Simulation on Mechanochemical Synthesis of Al2O3-TiB2 Nanocomposite using ANN with Bayesian Regularization & ANFIS
• An Artificial Intelligence Tool for Predicting Embryos Quality
• Passive System Reliability of the Nuclear Power Plants (NPPs) using Fuzzy Set Theory in Artificial Intelligence
• Emergent Tools in AI
• Neural Networks Applied to Micro-Computed Tomography
• Index.
• (source: Nielsen Book Data)

Artificial Intelligence may be defined as a collection of several analytic tools that collectively attempt to imitate life and has matured to a set of analytic tools that facilitate solving problems which were previously difficult or impossible to solve. In this book, the authors present topical research in the study of the tools and applications of artificial intelligence. Topics discussed include the application of artificial intelligence in the oil and gas industry and in metal stamping die design; using artificial intelligence to predict embryo quality and in biomedical imaging techniques.
(source: Nielsen Book Data)

• Scott Consequence Relations
• Biconsequence Relations
• Four-Valued Logics
• Nonmonotonic Semantics
• Default Consequence Relations
• Argumentation Theory
• Production and Causal Inference
• Epistemic Consequence Relations
• Modal Nonmonotonic Logics.
• (source: Nielsen Book Data)

Many approaches in the field of nonmonotonic and "commonsense" reasoning are actually different representations of the same basic ideas and constructions. This book gives a logical formalization of the original, explanatory approach to nonmonotonic reasoning. It uses the basic formalism of biconsequence relations, as well as derived systems of default, autoepistemic and causal inference, to cover in a single framework such diverse systems as default logic, autoepistemic and modal nonmonotonic logics, input/output and causal logics, argumentation theory, and semantics of general logic programs with negation as failure. This approach provides a clear separation between logical (monotonic) and nonmonotonic aspects of nonmonotonic reasoning. The separation allows, in particular, to single out the logics underlying modern logic programming and restore thereby the connection between logic programming and logic.
(source: Nielsen Book Data)

• Intro; Title Page; Acknowledgments; Contents; Abstract; Introduction; Federated SPARQL Query Processing; The Need for Efficient Source Selection; The Need for More Comprehensive SPARQL Benchmarks; Contributions; Chapter Overview; Basic Concepts and Notation; Semantic Web; URIs, RDF; SPARQL Query Language; Triplestore; SPARQL Syntax, Semantic and Notation; State of the Art; Federation systems evaluations; Benchmarks; Federated engines public survey; Survey Design; Discussion of the survey results; Details of selected systems; Overview of the selected approaches; Performance Variables.

• EvaluationExperimental setup; Evaluation criteria; Experimental results; Discussion; Effect of the source selection time; Effect of the data partitioning; Hypergraph-Based Source Selection; Problem Statement; HiBISCuS; Queries as Directed Labelled Hypergraphs; Data Summaries; Source Selection Algorithm; Pruning approach; Evaluation; Experimental Setup; Experimental Results; Trie-based Source Selection; TBSS; TBSS Data Summaries; TBSS Source Selection Algorithm; TBSS Pruning approach; QUETSAL; Quetsal's Architecture; Quetsal's SPARQL 1.1 Query Re-writing; Evaluation; Experimental Setup.

• Experimental ResultsDuplicate-Aware Source Selection; DAW; Min-Wise Independent Permutations (MIPs); DAW Index; DAW Federated Query Processing; Experimental Evaluation; Experimental Setup; Experimental Results; Policy-Aware Source Selection; Motivating Scenario; Methodology and Architecture; Evaluation; Experimental Setup; Experimental Results; Data Distribution-Based Source Selection; Motivation; Biological query example; Methods; Transforming TCGA data to RDF; Linking TCGA to the LOD cloud; TCGA data workflow and schema; Data distribution and load balancing.

• TopFed federated query processing approachSource selection; Results and discussion; Evaluation; Availability of supporting data; LargeRDFBench: SPARQL Federation Benchmark; Background; The Need of More Comprehensive SPARQL Federation Benchmark; Benchmark Description; Benchmark Datasets; Benchmark Queries; Performance Metrics; Evaluation; Experimental Setup; SPARQL 1.0 Experimental Results; SPARQL 1.1 Experimental Results; FEASIBLE: SPARQL Benchmarks Generation Framework; Key SPARQL Features; A Comparison of Existing Triple Stores Benchmarks and Query Logs; FEASIBLE Benchmark Generation.

• Data Set CleaningNormalization of Features Vectors; Query Selection; Complexity Analysis; Evaluation and Results; Composite Error Estimation; Experimental Setup; Experimental Results; Conclusion; HiBISCuS; TBSS/Quetsal; DAW; SAFE; TopFed; LargeRDFBench; FEASIBLE; Bibliography.

• History of Artificial Intelligence.- Symbolic Artificial Intelligence.- Computational Intelligence.- Search Methods.- Evolutionary Computing.- Logic-Based Reasoning.- Structural Models of Knowledge Representation.- Syntactic Pattern Analysis.- Rule-Based Systems.- Pattern Recognition and Cluster Analysis.- Neural Networks.- Reasoning with Imperfect Knowledge.- Defining Vague Notions in Knowledge-Based Systems.- Cognitive Architectures.- Theories of Intelligence in Philosophy and Psychology.- Application Areas of AI Systems.- Prospects of Artificial Intelligence.
• (source: Nielsen Book Data)

In the chapters in Part I of this textbook the author introduces the fundamental ideas of artificial intelligence and computational intelligence. In Part II he explains key AI methods such as search, evolutionary computing, logic-based reasoning, knowledge representation, rule-based systems, pattern recognition, neural networks, and cognitive architectures. Finally, in Part III, he expands the context to discuss theories of intelligence in philosophy and psychology, key applications of AI systems, and the likely future of artificial intelligence. A key feature of the author's approach is historical and biographical footnotes, stressing the multidisciplinary character of the field and its pioneers. The book is appropriate for advanced undergraduate and graduate courses in computer science, engineering, and other applied sciences, and the appendices offer short formal, mathematical models and notes to support the reader.
(source: Nielsen Book Data)

• Introduction.- Generalized Eigenvalue Proximal Support Vector Machines.- Twin Support Vector Machines (TWSVM) for Classification.- TWSVR: Twin Support Vector Machine Based Regression.- Variants of Twin Support Vector Machines: Some More Formulations.- TWSVM for Unsupervised and Semi-Supervised Learning.- Some Additional Topics.- Applications Based on TWSVM.- References.
• (source: Nielsen Book Data)

This book provides a systematic and focused study of the various aspects of twin support vector machines (TWSVM) and related developments for classification and regression. In addition to presenting most of the basic models of TWSVM and twin support vector regression (TWSVR) available in the literature, it also discusses the important and challenging applications of this new machine learning methodology. A chapter on "Additional Topics" has been included to discuss kernel optimization and support tensor machine topics, which are comparatively new but have great potential in applications. It is primarily written for graduate students and researchers in the area of machine learning and related topics in computer science, mathematics, electrical engineering, management science and finance.
(source: Nielsen Book Data)

• Case-Studies in Mining User-Generated Reviews for Recommendation
• Mining Newsworthy Topics from Social Media
• Sentiment Analysis Using Supervised Learning with Domain-Adaptation and Sentence-Based Analysis
• Pattern-based Emotion Classification on Social Media
• Entity-based Opinion Mining from Text and Multimedia
• Predicting Emotion Labels for Chinese Microblog Texts.

This volume presents a collection of carefully selected contributions in the area of social media analysis. Each chapter opens up a number of research directions that have the potential to be taken on further in this rapidly growing area of research. The chapters are diverse enough to serve a number of directions of research with Sentiment Analysis as the dominant topic in the book. The authors have provided a broad range of research achievements from multimodal sentiment identification to emotion detection in a Chinese microblogging website. The book will be useful to research students, academics and practitioners in the area of social media analysis.