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- Kruse, Rudolf, author.
- Third edition. - Cham : Springer, [2022]
- Description
- Book — 1 online resource : illustrations (some color).
- Summary
-
- Introduction
- Part I: Neural Networks
- Introduction
- Threshold Logic Units
- General Neural Networks
- Multi-Layer Perceptrons
- Radial Basis Function Networks
- Self-Organizing Maps
- Hopfield Networks
- Recurrent Networks
- Mathematical Remarks for Neural Networks
- Part II: Evolutionary Algorithms
- Introduction to Evolutionary Algorithms
- Elements of Evolutionary Algorithms
- Fundamental Evolutionary Algorithms
- Computational Swarm Intelligence
- Part III: Fuzzy Systems
- Fuzzy Sets and Fuzzy Logic
- The Extension Principle
- Fuzzy Relations
- Similarity Relations
- Fuzzy Control
- Fuzzy Data Analysis
- Part IV: Bayes and Markov Networks
- Introduction to Bayes Networks
- Elements of Probability and Graph Theory
- Decompositions
- Evidence Propagation
- Learning Graphical Models
- Belief Revision
- Decision Graphs.
- Singapore : Springer, [2021]
- Description
- Book — 1 online resource (xii, 164 pages) : illustrations (some color)
- Summary
-
- Single Identity Clustering-based Data Anonymization in Healthcare.- Optimization Model for Production Planning: Case of an Indian Steel Company.- Vision-based User-Friendly and Contactless Security for Home Appliance via Hand Gestures.- Sentiment Analysis of Healthcare Big Data: A Fundamental Study.- Sleep Apnea Detection using Contact-Based and Non-Contact Based Using Deep Learning Methods.- Drift Compensation of a Low-cost pH Sensor by Artificial Neural Network.- Sentiment Analysis at Online Social Network for Cyber Malicious Post Reviews Using Machine Learning Techniques.- Analysis of various mobility models and their impact on QoS in MANET.- Analysis of Classifier Algorithms to detect Anti Money Laundering.- Design and Development of an ICT Intervention for Early Childhood Development in Minority Ethnic Communities in Bangladesh.
- (source: Nielsen Book Data)
(source: Nielsen Book Data)
- Cham : Springer, 2019.
- Description
- Book — 1 online resource
- Summary
-
- Foreword.- Preface. - Control engineering from classical to intelligent control theory - An overview (Blondin, Saez, Pardalos).- Main metaheuristics used for the optimization of the control of the complex systems (Borne, Gharbi).- Optimal controller parameter tuning from multi/many objective optimization algorithms (Altinoz).- Fuzzy and neuro-fuzzy control for smart structures (Tairidis, Stavroulakis).- Computational intelligence in the desalination industry (Cabrera, Carta).- Control of complex biological systems utilizing the neural network predictor (Bamgbose, Li, Qian).- A real-time big-data control-theoretical framework for cyber-physical-human systems (Gusrialdi, Xi, Qu, Simaan).- Distributed optimization based control on the example of microgrids (Braun, Sauerteig, Worthmann).- Coherency estimation in power systems: A Koopman operator approach (Chamorro, Ordonez, Peng, Gonzalez-Longatt, Sood).- Appliance identification through non-intrusive load monitoring in residences (Gogos, Georgiou).- Management suggestions for process control of semiconductor manufacturing: An operations research and data science perspective (Khakifirooz, Fathi, Chien, Pardalos).- Feedback control algorithms for the dissipation of traffic waves with autonomous vehicles (Monache, Liard, Rat, Stern, Bhadani, Seibold, Sprinkle, Work, Piccoli).- Disturbance rejection run-to-run controller for semiconductor manufacturing (Khakifirooz, Fathi, Pardalos).- Energy management improvement based on fleet learning for hybrid electric buses (Lopez-Ibarra, Herrera, Milo, Gaztanaga, Camblong). .
- (source: Nielsen Book Data)
(source: Nielsen Book Data)
- Second edition. - London, United Kingdom : Springer, 2016.
- Description
- Book — 1 online resource (xiii, 564 pages) : illustrations Digital: text file.PDF.
- Summary
-
- Introduction
- Part I: Neural Networks
- Introduction
- Threshold Logic Units
- General Neural Networks
- Multi-Layer Perceptrons
- Radial Basis Function Networks
- Self-Organizing Maps
- Hopfield Networks
- Recurrent Networks
- Mathematical Remarks for Neural Networks
- Part II: Evolutionary Algorithms
- Introduction to Evolutionary Algorithms
- Elements of Evolutionary Algorithms
- Fundamental Evolutionary Algorithms
- Computational Swarm Intelligence
- Part III: Fuzzy Systems
- Fuzzy Sets and Fuzzy Logic
- The Extension Principle
- Fuzzy Relations
- Similarity Relations
- Fuzzy Control
- Fuzzy Data Analysis
- Part IV: Bayes and Markov Networks
- Introduction to Bayes Networks
- Elements of Probability and Graph Theory
- Decompositions
- Evidence Propagation
- Learning Graphical Models
- Belief Revision
- Decision Graphs.
- Berlin : Springer, 2016.
- Description
- Book — 1 online resource (xii, 251 pages) : illustrations Digital: text file.PDF.
- Summary
-
- Robustness of Legislative Procedures of the Italian Parliament.- Approval Voting as a Method of Prediction in Political Votings. Case of Polish elections.- The Complexity of Voter Control and Shift Bribery under Parliament Choosing Rules.- National Interests in the European Parliament: Roll Call Vote Analysis.- Voting and Communication when Hiring by Committee.- Power Measures and Public Goods.- Holdout Threats During Wage Bargaining.- Index of implicit power as a measure of reciprocal ownership.- Manipulability Of Voting Procedures: Strategic Voting And Strategic Nomination.- Reflections on the Signifcance of Misrepresenting Preferences.- Fibonacci representations of homogeneous weighted majority games.- Towards a fairness-oriented approach to consensus reaching support under fuzzy preferences and a fuzzy majority via linguistic summaries?.- What Is It That Drives Dynamics: We Don't Believe in Ghosts, Do We?.
- (source: Nielsen Book Data)
(source: Nielsen Book Data)
- Berlin, Germany : Springer, 2016.
- Description
- Book — 1 online resource (ix, 169 pages) : illustrations Digital: text file.PDF.
- Summary
-
- Dynamic Topologies for Particle Swarms
- Evaluative Study of PSO/Snake Hybrid Algorithm and Gradient Path Labeling for Calculating Solar Differential Rotation
- The Uncertainty Quandary: A Study in the Context of the Evolutionary Optimization in Games and other Uncertain Environments
- Hybrid Single Node Genetic Programming for Symbolic Regression
- L2 Designer: A Tool for Genetic L-system Programming in Context of Generative Art
- Manifold Learning Approach toward Constructing State Representation for Robot Motion Generation
- The Existence of Two Variant Processes in Human Declarative Memory: Evidence Using Machine Learning Classification Techniques in Retrieval Tasks
- Divide and Conquer Ensemble Method for Time Series Forecasting
- Application areas of Ephemeral Computing: A survey.
- Berlin : Springer-Verlag, 2008.
- Description
- Book — 1 online resource (xviii, 375 pages) : illustrations Digital: text file.PDF.
- Summary
-
- Particle Swarm Optimization and Differential Evolution Algorithms: Technical Analysis, Applications and Hybridization Perspectives
- Web Services, Policies, and Context: Concepts and Solutions
- Data Mining with Privacy Preserving in Industrial Systems
- Kernels for Text Analysis
- Discovering Time-Constrained Patterns from Long Sequences
- Gauging Image and Video Quality in Industrial Applications
- Model Construction for Knowledge-Intensive Engineering Tasks
- Artificial Intelligence Applied to the Modeling and Implementation of a Virtual Medical Office
- DICOM-Based Multidisciplinary Platform for Clinical Decision Support: Needs and Direction
- Improving Neural Network Promoter Prediction by Exploiting the Lengths of Coding and Non-Coding Sequences
- Artificial Immune Systems for Self-Nonself Discrimination: Application to Anomaly Detection
- Computational Intelligence Applied to the Automatic Monitoring of Dressing Operations in an Industrial CNC Machine
- Automated Novelty Detection in Industrial Systems
- Multiway Principal Component Analysis (MPCA) for Upstream/Downstream Classification of Voltage Sags Gathered in Distribution Substations
- Applications of Neural Networks to Dynamical System Identification and Adaptive Control
- A Multi-Objective Multi-Colony Ant Algorithm for Solving the Berth Allocation Problem
- Query Rewriting for Semantic Multimedia Data Retrieval.
- Berlin : Springer, ©2008.
- Description
- Book — 1 online resource (xxxvi, 1179 pages) : illustrations Digital: text file.PDF.
- Summary
-
- Part I Overview, Background.- Part II Preprocessing
- Visualization
- Systems Integration.- Part III Artificial Intelligence.- Part IV Logic and Reasoning.- Part V Ontology.- Part VI Intelligent Agents.- Part VII Fuzzy Systems.- Part VIII Artificial Neural Networks.- Part IX Evolutionary Approaches.- Part X DNA and Immunity-based Computing.
- (source: Nielsen Book Data)
- Berlin : Springer, ©2008.
- Description
- Book — 1 online resource (viii, 279 pages) : illustrations Digital: text file.PDF.
- Summary
-
- An Introduction to Computational Intelligence Paradigms and their Applications.- A Quest for Adaptable and Interpretable Architectures of Computational Intelligence.- Membership Map.- Advanced Dev of Fuzzy ARTMAP --Pattern Classification Boaz.- Large Margin Methods for Structured Output Prediction.- nsemble Classifier Design.- Functional Principal Points and Functional Cluster Analysis.- Clustering with Size Constraints.- luster Validating Techniques.- Fuzzy Blocking Regression Models.- Support Vector Machines.- Linkage Analysis in Genetic Algorithms.
- (source: Nielsen Book Data)
(source: Nielsen Book Data)
- Berlin : Springer-Verlag, ©2008.
- Description
- Book — 1 online resource (xii, 326 pages) : illustrations Digital: text file.PDF.
- Summary
-
- Part I Motivations and Theory.- Part II Search and Reasoning.- Part III Optimization.- Part IV Learning.- Part V RealWorld Applications.
- (source: Nielsen Book Data)
(source: Nielsen Book Data)
- Kristensen, Terje.
- Sharjah : Bentham Science Publishers, 2016.
- Description
- Book — 1 online resource (135 pages)
- Summary
-
- PREFACE ; ACKNOWLEDGEMENTS; CONFILICT OF INTEREST; Introduction ; 1.1. OVERVIEW; 1.2. GOAL; 1.3. OUTLINE;
- Chapter 1 (Introduction);
- Chapter 2 (Background);
- Chapter 3 (Evolutionary Algorithms);
- Chapter 4 (System Specification);
- Chapter 5 (Design and Implementation);
- Chapter 6 (Data Visualization);
- Chapter 7 (User Interface);
- Chapter 8 (Case Study);
- Chapter 9 (Discussion);
- Chapter 10 (Summary and Future); Background ; 2.1. CLUSTERING; 2.1.1. Introduction; 2.1.2. General Definition; 2.1.3. Object Similarity; Proximity Measure for Continuous Values; Proximity Measure for Discrete Values.
- Proximity Measure for Mixed Values2.1
- .4. Clustering Methods; Hierarchical Clustering; Partitional Clustering; Fuzzy Clustering; 2.1
- .5. Cluster Membership; 2.1
- .6. Cluster Validation; Evolutionary Algorithms ; 3
- .1. INTRODUCTION; 3.1
- .1. Data Representation Chromosome; 3.1
- .2. Initial Population; 3.1
- .3. Fitness Function; 3.1
- .4. Selection; 3.1
- .5. Reproduction; 3.1
- .6. Stopping conditions; 3
- .2. MATHEMATICAL OPTIMIZATION; 3.2
- .1. Maxima and Mimima; 3.2
- .2. Optimization Problems; 3
- .3. GENETIC ALGORITHMS; 3.3
- .1. Crossover; 3.3
- .2. Mutation; 3.3
- .3. Control Parameters; 3
- .4. GENETIC PROGRAMMING.
- 3.4
- .1. Tree Based Representation3.4
- .2. Fitness Function; 3.4
- .3. Crossover Operators; 3.4
- .4. Mutation Operators; 3
- .5. EVOLUTIONARY PROGRAMMING; 3.5
- .1. Representation; 3.5
- .2. Mutation Operators; 3.5
- .3. Selection Operators; 3
- .6. EVOLUTION STRATEGIES; 3.6
- .1. Generic Evolution Strategies Algorithm; 3.6
- .2. Strategy Parameter; 3.6
- .3. Selection Operator; 3.6
- .4. Crossover Operators; 3.6
- .5. Mutation Operator; 3
- .7. DIFFERENTIAL EVOLUTION; 3.7
- .1. Mutation Operator; 3.7
- .2. Crossover Operator; 3.7
- .3. Selection; 3.7
- .4. Control Parameters; 3
- .8. CULTURAL ALGORITHMS; 3.8
- .1. Belief Space.
- 3.8
- .2. Acceptance Function3.8
- .3. Influence Function; System Specification ; 4
- .1. INTRODUCTION; 4
- .2. SYSTEM OBJECTIVE; 4
- .3. FUNCTIONAL REQUIREMENTS; 4.3
- .1. System Input; 4.3
- .2. Cluster Analysis; 4.3
- .3. Visualization; 4
- .4. NON-FUNCTIONAL REQUIREMENTS; 4.4
- .1. Functional Correctness; 4.4
- .2. Extensibility; 4.4
- .3. Maintainability; 4.4
- .4. Portability; 4.4
- .1. Usability; Design and Implementation ; 5
- .1. INTRODUCTION; 5
- .2. SYSTEM ARCHITECTURE; 5.2
- .1. Dependency Injection; 5.2
- .2. Open-Closed Principle; 5
- .3. TOOLS AND TECHNOLOGIES; 5.3
- .1. Java; 5.3
- .2. JavaFX; 5.3
- .3. Netbeans; 5.3
- .4. Maven.
- 5.3
- .5. Git and GitHub5.3
- .6. JUnit; 5
- .4. DATA STRUCTURE AND CLUSTERING; 5.4
- .1. Import Data and Data Structure; 5.4
- .2. K-means Algorithm; Complexity of K-Means Operations; 5
- .5. EVOLUTIONARY ALGORITHMS; 5.5
- .1. Genetic Clustering Algorithm; Population Initialization; Fitness Evaluation; Evolve Population; Termination Criteria; Time-Complexity; 5.5
- .2. Differential Evolution Based Clustering Algorithm; Population Initialization; Mutation; Crossover; Termination Criteria; Time-complexity; 5.5
- .3. Selection Operators; Random Selection; Proportional Selection; 5.5
- .4. Mutation Operators.
12. Computational intelligence : synergies of fuzzy logic, neural networks, and evolutionary computing [2013]
- Siddique, N. H.
- Chichester, West Sussex, United Kingdom : John Wiley & Sons Inc., 2013.
- Description
- Book — 1 online resource
- Summary
-
- 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)
(source: Nielsen Book Data)
- Okwu, Modestus O., author.
- Cham : Springer, [2021]
- Description
- Book — 1 online resource (196 pages) Digital: text file.PDF.
- Summary
-
- Introduction To Optimization.- Particle Swarm Optimisation.- Artificial Bee Colony Algorithm.- Ant Colony Algorithm.- Grey Wolf Optimizer.- Whale Optimization Algorithm.- Bat Algorithm.- Ant Lion Optimization Algorithm.- Grasshopper Optimisation Algorithm (Goa).- Moths-Flame Optimization Algorithm.- Genetic Algorithm.- Artificial Neural Network.- Future of Nature Inspired Algorithm, Swarm and Computational Intelligence.
- (source: Nielsen Book Data)
(source: Nielsen Book Data)
- Herawan, Tutut, author.
- Cham, Switzerland : Springer Nature, [2019]
- Description
- Book — 1 online resource
- Summary
-
- Intro; Preface; Reviewers; GET Authors; Contents; A Theoretical Framework for Big Data Analytics Based on Computational Intelligent Algorithms with the Potential to Reduce Energy Consumption; 1 Introduction; 2 Computational Intelligent Algorithms; 2.1 Characteristics of Computational Intelligent Algorithms; 3 Big Data Analytics and Energy Consumption by Cluster Computing Systems; 3.1 Big Data Analytics Platforms; 3.2 Energy Consumption Over Big Data Platforms; 3.3 Metrics Used for Measuring Power in Big Data Platforms; 4 Computational Intelligent Algorithms and Big Data Analytics
- 5 Energy Consumption in the Application of Computational Intelligent Algorithms in Big Data Analytics6 A Proposed Framework for Big Data Analytics Using Computational Intelligent Algorithms; 7 Conclusions; References; Artificial Bee Colony for Minimizing the Energy Consumption in Mobile Ad Hoc Network; 1 Introduction; 2 Energy-Aware Routing Protocol; 3 Routing Protocols in MANET; 3.1 Destination-Sequenced Distance-Vector Routing; 3.2 Ad Hoc On-Demand Distance-Vector Routing Protocol; 4 Artificial Bee Colony for AODV and DSDV; 5 Experimental Results; 5.1 Simulation Settings
- 5.2 Performance Metrics5.3 Simulation Results and Performance Comparison; 6 Conclusion; References; A Novel Chicken Swarm Neural Network Model for Crude Oil Price Prediction; 1 Introduction; 2 Artificial Neural Network; 3 Chicken Swarm Optimization; 4 The Proposed Chicken S-NN Algorithm; 5 Results & Discussion; 5.1 Preliminaries; 5.2 Data; 5.3 Discussion; 6 Conclusion; References; Forecasting OPEC Electricity Generation Based on Elman Network Trained by Cuckoo Search Algorithm; 1 Introduction; 2 Elman Network; 3 Cuckoo Search; 4 The Proposed CS Elman Algorithm; 5 Results and Discussion
- 5.1 Discussion6 Conclusions; References; Variable Neighborhood Search-Based Symbiotic Organisms Search Algorithm for Energy-Efficient Scheduling of Virtual Machine in Cloud Data Center; 1 Introduction; 2 Related Works; 3 Energy-Efficient Virtual Machine Scheduling Optimization; 3.1 Problem Definition; 3.2 Basic Concepts of Symbiotic Organisms Search; 4 Performance Evaluation; 4.1 Experimental Setup; 4.2 Results and Discussion; 5 Conclusion and Future Work; References; Energy Savings in Heterogeneous Networks with Self-Organizing Backhauling; 1 Introduction
- 2 Base Station Types in HETNET and Power System Consideration2.1 Base Station Types in HetNet; 2.2 Power System Consideration of BS Sites; 3 Small Cells Deployment and Backhauling Options; 3.1 Wired Backhaul Options for Small Cells; 3.2 Wireless Backhaul Options; 4 System Concept; 5 Backhaul-Energy Model; 6 Results and Discussions; 6.1 Typical Power Consumption of Macro BS and Microwave Backhaul Hub Sites; 6.2 Power Consumption of HetNet and the Break-Even Load; 6.3 Impact of Macro Base Station Load on Power Consumption; 6.4 Energy Savings of Self-Backhauling; 7 Conclusions; References
- Cham : Springer, [2022]
- Description
- Book — 1 online resource (432 pages) : illustrations (chiefly color) Digital: text file.PDF.
- Summary
-
- Part I: State-of-the-art.- Online Fake News Detection Using Machine Learning Techniques: A Systematic Mapping Study.- Using Artificial Intelligence against the Phenomenon of Fake News: a Systematic Literature Review.- Fake news detection in internet using deep learning: A review.- Part II: Machine Learning Techniques and Fake News.- Early Detection of Fake News from Social Media Networks using Computational Intelligence Approaches.- Fandet Semantic Model: An OWL Ontology for Context-Based Fake News Detection on Social Media.- Fake News Detection using Machine Learning and Natural Language Processing.- Fake News Detection using Ensemble Learning and Machine Learning Algorithms.- Evaluation of Machine Learning Methods for Fake News Detection.- Credibility and Reliability News Evaluation Based on Artificial Intelligent Service with Feature Segmentation Searching and Dynamic Clustering.- Deep Learning with Self-Attention Mechanism for Fake News Detection.- Modeling and solving the fake news detection scheduling problem.- Part III: Case Studies and Frameworks.- The multiplier effect on the dissemination of false speeches on social networks: Experiment during the silly season in Spain.- Detecting News Influence in a Country: One Step Forward Towards Understanding Fake News.- Factors Affecting the Intention of Using Fintech Services in the Context of Combating of Fake News.- Crowd Sourcing and Blockchain-based Incentive Mechanism to Combat Fake News.- Framework for Fake News Classification using Vectorization and Machine Learning.- Fact Checking: An Automatic end to end Fact Checking System.- Part IV: Fake news and Covid-19 pandemic.- False Information in a Post Covid-19 World.- Applying Fuzzy Logic and Neural Network in Sentiment Analysis for fake news detection: Case of Covid-19.- Analyzing Deep Learning Optimizers for COVID-19 Fake News Detection.- Detecting Fake News On COVID-19 Vaccine from YouTube Videos Using Advanced Machine Learning Approaches.
- (source: Nielsen Book Data)
(source: Nielsen Book Data)
- UK Workshop on Computational Intelligence (20th : 2021 : Online)
- Cham, Switzerland : Springer, [2022]
- Description
- Book — 1 online resource (xi, 579 pages) : illustrations (some color) Digital: text file.PDF.
- Summary
-
- An Evolving Feature Weighting Framework for Granular Fuzzy Logic Models.- Fuzzy Multi-Criteria Decision-Making: Example of an explainable classification framework.- Rough-Fuzzy Rule Interpolation for Data-Driven Decision Making.- Fuzzy Intelligent System for Improving High Availability of a Docker-Based Hybrid Cloud Infrastructure.- A Study of Neuro-weighted Nearest-neighbour Classification.- Q-Routing with Multiple Soft Requirements.- Avoiding Excess Computation in Asynchronous Evolutionary Algorithms.- Stochasticity improves evolvability in articial gene regulatory networks.- ACO Inspired GA Mutation Applied to the TSP.- Centralised and Decentralised Control of Video Game Agents.
- (source: Nielsen Book Data)
(source: Nielsen Book Data)
- IEEE Colombian Conference on Applications in Computational Intelligence (4th : 2021 : Online)
- Cham : Springer, [2022]
- Description
- Book — 1 online resource : illustrations (chiefly color).
- Summary
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- Biomedical Applications
- Anemia Detection using a Full Embedded Mobile Application with YOLO Algorithm
- On the Use of Convolutional Neural Network Architectures for Facial Emotion Recognition
- Automated Preprocessing Pipeline in Visual Imagery Tasks
- Multilabel and Multiclass Approaches Comparison for Respiratory Sounds Classification
- Alternative Proposals and its Applications. - Weighted Hausdor Distance Loss as a Function of Di
- ICCSIP (Conference) (6th : 2021 : Suzhou, China)
- Singapore : Springer, 2022.
- Description
- Book — 1 online resource Digital: text file; PDF.
- Summary
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- Algorithm.- Vision.- Robotics and Application.
- (source: Nielsen Book Data)
(source: Nielsen Book Data)
19. Intelligence enabled research : DoSIER 2021 [2022]
- Doctoral Symposium on Intelligence Enabled Research (3rd : 2021 : Koch Bihar, India)
- Singapore : Springer, 2022.
- Description
- Book — 1 online resource (187 pages)
- Summary
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- Solving Graph Coloring Problem Using Ant Colony Optimization, Simulated Annealing And Quantum Annealing - A Comparative Study.- Computer Assisted Diagnosis and Neuroimaging of Baby Infants.- Early Prediction of Ebola Virus Using Advanced Recurrent Neural Networks.- A Three-Step Fuzzy based BERT Model for Sentiment Analysis.- Mayfly Algorithm Based PID Controller for LFC of Multi Sources Single Area Power System.- Group Key Management Techniques for Secure Load Balanced Routing Model.- Search Techniques for Data Analytics with focus on Ensemble Methods.- A Survey on Underwater Object Detection.- COVACDISER: A Machine Learning based Web Application to Recommend the Prioritization of COVID-19 Vaccination.- Research of High-Speed Procedures for Defuzzification Based on the Area Ratio Method.- A Single Qubit Quantum Perceptron for OR and XOR Logic.- Societal Gene Acceptance Index Based Crossover in GA for Traveling Salesman Problem.- The method of automated neuro-fuzzy calibration of geometrically distorted images of digital X-ray tomographs.
- (source: Nielsen Book Data)
(source: Nielsen Book Data)
- International Conference on Intelligent Computing and Optimization (4th : 2021 : Online)
- Cham, Switzerland : Springer, 2022.
- Description
- Book — 1 online resource Digital: text file; PDF.
- Summary
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- Low-Light Image Enhancement with Artificial Bee Colony Method.- Optimal State-Feedback Controller Design for Tractor Active Suspension System via Levy-Flight Intensified Current Search Algorithm.- The Artificial Intelligence Platform with the Use of DNN to Detect Flames: A Case of Acoustic Extinguisher.- Adaptive harmony search for cost optimization of reinforced concrete columns.- Best Traffic Signs Recognition Based on CNN Model for Self-Driving Cars.- Optimisation and Prediction of Glucose Production from Oil Palm trunk via Simultaneous Enzymatic Hydrolysis.- Synthetic data augmentation of cycling sport training datasets.- Optimal Compensation of Bouc-Wen model hysteresis using square dither.- Hybrid Pooling Based Convolutional Neural Network for Multi-class Classification of MR Brain Tumor Images.- Importance of Fuzzy Logic in Traffic and Transportation Engineering.- A Fuzzy Based Clustering Approach to Prolong the Network Lifetime in WSNs.- Visual Expression Analysis from Face Images Using Morphological Processing.- Detection of invertebrate virus carriers using deep learning networks to prevent emerging pandemic-prone disease in tropical regions.- Classification and detection of Plant Leaf Diseases using various Deep Learning techniques and Convolutional Neural Network.- Distributed Self-triggered Optimization for Multi-agent Systems.- Automatic Categorization of News Articles and Headlines using Multi-layer Perceptron.- Using Machine Learning Techniques for Estimating the Electrical Power of a New-Style of Savonius Rotor: A Comparative Study.- Tree-like Branching Network for Multi-class Classification.- Multi-Resolution Dense Residual Networks with High- Modularization for Monocular Depth Estimation.- A Decentralized Federated Learning paradigm for Semantic Segmentation of Geospatial Data.- Development of Contact Angle Prediction for Cellulosic Membrane.- Feature Engineering Based Credit Card Fraud Detection for Risk Minimization in E-Commerce.- DCNN-LSTM Based Audio Classification Combining Multiple Feature Engineering and Data Augmentation Techniques.- Sentiment Analysis: Developing an Efficient Model Based on Machine Learning and Deep Learning Approaches.- Improved Face Detection System.- Paddy Price Prediction in the South-Western Region of Bangladesh.- Paddy Disease Prediction Using Convolutional Neural Network.- Android Malware Detection System: A Machine Learning and Deep Learning based Multilayered Approach.
- (source: Nielsen Book Data)
(source: Nielsen Book Data)
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