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• Preface.Acknowledgment.About the Authors
• .1. Introduction
• .2. Introduction to Wavelet Analysis.2.1 Introduction.2.2 Basic Concept of Wavelets and Wavelet Analysis.2.2.1 What is a Wavelet?.2.2.2 Wavelet Analysis.2.2.3 Types of Wavelets and Wavelet Transforms.2.3 Mathematical Foundations.2.3.1 Sets and Spaces.2.3.2 Sequence and Function Spaces.2.3.3 Independent and Basis Sets.2.3.4 Metric, Normed and Inner Product Spaces.2.3.5 The L2(R) and L2(Z) Spaces.2.3.6 Orthogonality.2.4 The Discrete Wavelet Transform (DWT).2.5 Multi-resolution Analysis.2.6 Wavelet Bases.2.6.1 Constructing Wavelet Bases.2.6.2 Example Wavelet Systems.2.7 Computing the DWT.2.7.1 Pyramid Algorithm.2.7.2 Practical Considerations
• .3. Feature Extraction for Traffic Incident Detection Using Wavelet Transform and Linear Discriminant Analysis.3.1 Introduction.3.2 Incident Detection Algorithms.3.3 Discrete Wavelet Transform (DWT) of Traffic Signals.3.4 Linear Discriminant Analysis (LDA).3.5 Data Acquisition.3.6 Results
• .4. Adaptive Conjugate Neural Network-Wavelet Model for Traffic Incident Detection.4.1 Introduction.4.2 Improving Traffic Incident Detection.4.3 Adaptive Conjugate Gradient Neural Network Model.4.4 Incident Detection Results Using Various Approaches.4.4.1 LDA.4.4.2 DWT and LDA.4.4.3 ACGNN.4.4.4 DWT, LDA, and ACGNN.4.5 Effect of Data Filtering Using DWT.4.6 Relative Contribution of DWT and LDA for Feature Extraction.4.7 Effects of Freeway Geometry on Incident Detection.4.7.1 Effect of Curvature.4.7.2 Effect of Number of Lanes.4.8 Conclusion
• .5. Enhancing Fuzzy Neural Network Algorithms Using Neural Networks.5.1 Introduction.5.2 Discrete Wavelet Transform.5.3 Architecture.5.4 Training of the Network.5.5 Filtering of the Traffic Data Using DWT.5.6 Incident Detection Results
• .6. Fuzzy-Wavelet Radial Basis Function Neural Network Model for Freeway Incident Detection.6.1 Introduction.6.2 A New Traffic Incident Detection Methodology.6.3 Selection of Type and Number of Traffic Data.6.4 Wavelet-Based De-noising.6.5 Fuzzy Data Clustering.6.6 Radial Basis Function Neural Network Classifier.6.7 Fuzzy-Wavelet RBFNN Model for Incident Detection.6.8 Example.6.8.1 Training of Algorithm.6.8.2 Testing of Algorithm Using Simulated Data.6.8.3 Testing of Algorithm Using Real Data.6.9 Conclusion
• .7. Comparison of Fuzzy-Wavelet RBFNN Freeway Incident Detection Model with California Algorithm.7.1 Introduction.7.2 California Algorithm #8.7.3 Evaluation of the Model.7.3.1 Introduction.7.3.2 Evaluation Criteria.7.3.3 Traffic Data.7.3.4 Training and Calibration.7.3.5 First Simulation Test - Parametric Evaluation.7.3.6 Second Simulation Test - Freeway with On- and Off-Ramps.7.3.7 Test Using Real Data.7.4 Concluding Remarks
• .8. Incident Detection Algorithm Using Wavelet Energy Representation of Traffic Patterns.8.1 Introduction.8.2 Freeway Incident Detection and Patterns in Traffic Flow.8.2.1 Single-Station Versus Two-Station Incident Detection Approaches.8.2.2 Upstream and Downstream Flow Patterns.8.3 Discrete Wavelet Transform and Signal Energy.8.4 Traffic Pattern Feature Enhancement and De-noising.8.5 Pattern Classification Using Radial Basis Function Neural Networks.8.6 Wavelet Energy Freeway Incident Detection Algorithm.8.7 Model Testing.8.7.1 Introduction.8.7.2 Training.8.7.3 First Test Using Simulated Data: Two-lane Freeway.8.7.4 Second Test Using Simulated Data: Three-lane Freeway.8.7.5 Third Test Using Simulated Data: Compression Waves.8.7.6 Fourth Test Using Real Data: FSP Project's I-880 Database.8.7.7 Result Summary and Comparison.8.8 Conclusion
• .9. Parametric Evaluation of the Wavelet Energy Freeway Incident Detection Algorithm.9.1 Introduction.9.2 Factors to Consider in Rural Freeway Incident Detection.9.3 Evaluation and Parametric Investigation.9.3.1 Goals.9.3.2 Data.9.3.3 Training and Calibration.9.4 Parametric Evaluation Using Simulated Data on Typical Urban Freeways.9.5 False Alarm Performance in the Vicinity of On- and Off-Ramps.9.6 Evaluation on Rural Freeways.9.7 Evaluation Using Real Data.9.8 Performance Summary and Conclusion
• .10. Case-Based Reasoning Model for Work Zone Traffic Management.10.1 Introduction.10.2 Work Zones and Traffic Management.10.3 Case-Based Reasoning.10.4 Objectives.10.5 Scope and Categorization of Parameters.10.5.1 Work Zone Type.10.5.2 Work Zone Layout.10.5.3 Work Characteristics.10.5.4 Traffic flow characteristics.10.5.5 Phases of Work.10.5.6 Traffic Control Measures.10.5.7 Road User Cost.10.6 A Four-Set Case Model for the Work Zone Traffic Management Domain.10.7 Hierarchical Object-Oriented Case Model.10.8 Case Representation.10.9 Similarity Measures.10.10 Case Retrieval.10.11 Creation of the Case Base.10.12 Creation of Work Zone Traffic Control Plans Using the CBR System.10.13 Illustrative Examples.10.13.1 Example 1.10.13.2 Example 2.10.13.3 Example 3.10.14 Conclusion
• .11. Mesoscopic-Wavelet Freeway Work Zone Flow and Congestion Model.11.1 Introduction.11.2 Macroscopic Models.11.3 Microscopic Models.11.4 A Mesoscopic Flow Model for a Freeway Work Zone.11.5 Traffic Feature Enhancement Using Discrete Wavelet Transform.11.6 Concluding Remarks.References.Index.
• (source: Nielsen Book Data)

This book shows how wavelets can be used to enhance computational intelligence for chaotic and complex pattern recognition problems. By integrating wavelets with other soft computing techniques such as neurocomputing and fuzzy logic, complicated and noisy pattern recognition problems can be solved effectively. The book focuses on applications in intelligent transportation systems (ITS) where a number of very complicated pattern recognition problems have eluded researchers over the past few decades. Advancing the frontiers of computational intelligence, this book describes ingenious computational models based on novel problem solving and computing techniques such as Case-Based Reasoning, Neurocomputing, and Wavelets, and presents examples to illustrate their importance and use.It presents a multi-paradigm intelligent systems approach to the freeway traffic incident detection and construction work zone management problems. It advocates application and integration of wavelets, neural networks and fuzzy logic for modeling the complex traffic flow behaviors leading to effective and efficient control and management solutions. It presents efficient, reliable, and robust algorithms for automatic detection of incidents on freeways. "Wavelets in Intelligent Transportation Systems" is an invaluable resource for computational intelligence researchers and transportation engineers involved in the application of advanced computational techniques for ITS.
(source: Nielsen Book Data)

• Microtasking, Macrotasking, and Autotasking
• Formulation of the Integrated Structural/Control Optimization
• Parallel Algorithms for Solution of the Eigenvalue Problem
• Parallel Algorithms for Solution of the Riccati Equation
• Smart Bridge Structures
• Smart Multistory Building Structures Under Earthquake and Wind Loadings
• Smart Building Structures Under Blast Loading
• Simultaneous Optimization of Control System and Structure
• Bibliography
• Subject Index.
• (source: Nielsen Book Data)

"Smart" or "adaptive" structures are the next generation of structural systems. They are designed with embedded computer systems and sensors within them that help these structures to modify their behaviour - in effect, to react to stimuli and events during earthquakes, wind events, and blasts. A smart building or bridge, upon first sensing seismic tremors, would vibrate and sway in ways designed to keep it intact through the duration of the earthquake to come. Using the same computer technology that made smart bombs famous during the Gulf War episode, these smart structures are being built with increasing frequency now that this technology is gaining acceptance. This book explains the technology, and how to work it into a structural design efficiently and effectively.
(source: Nielsen Book Data)

• Introduction Distributed Finite Element Analysis on a Network of Workstations Implementation of Distributed Algorithms for Finite Element Analysis on a Network of Workstations Distributed Genetic Algorithms for Design Optimization Distributed Genetic Algorithms on a Cluster of Workstations Concurrent Structural Optimization on Massively Parallel Computers Concurrent Animation of Seismic Response of Large Structures in a Heterogeneous Computing Environment New Directions and Final Thoughts References.
• (source: Nielsen Book Data)

Networking of personal computers and workstations is becoming commonplace in academic and industrial environments. A cluster of workstations provides engineers with a familiar, cost-effective environment for high performance computing. However, workstations often have no dedicated link and communicate slowly on a local area network (LAN), such as the Ethernet. Thus, to effectively harness the parallel processing or distributed computing capabilities of workstations, new algorithms need to be developed with a higher computation-to-communication ratio.Distributed Computer-Aided Engineering presents distributed algorithms for three fundamental areas: finite element analysis, design optimization, and visualization - providing a new direction in high performance structural engineering computing.
(source: Nielsen Book Data)

• Introduction Stiffness Method Solution of Simultaneous Linear Equations Vectorization Techniques Parallel-Vector Algorithms for Analysis of Large Structures Impact of Vecorization on Large-Scale Structural Optimization Optimization of Large Steel Structures Using Standard Cross Sections Optimum Load and Resistance Factor Design of Large Steel Structures.
• (source: Nielsen Book Data)

High-performance multiprocessor computers provide new and interesting opportunities to solve large-scale structural engineering problems. However, the development of new computational models and algorithms that exploit the unique architecture of these machines remains a challenge. "High Performance Computing in Structural Engineering" explores the use of supercomputers with vectorization and parallel processing capabilities in structural engineering applications. The book focuses on the optimization of large structures subjected to the complicated, implicit, and discontinuous constraints of commonly used design codes and presents robust parallel-algorithms for analysis of these structures.The authors apply the algorithms to and analyze the performance of minimum weight designs of large, steel space trusses and moment-resisting frames, with or without bracings, consisting of discrete standard shapes. They clearly show that adroit and judicious use of vectorization techniques can improved the speedup of an optimization algorithm, and that parallel processing can lead to even further speedup. With its review of the necessary background material, generous illustrations, and unique content, this is the definitive resource for the analysis and optimization of structure on shared-memory multiprocessor computers. By extension, "High Performance Computing in Structural Engineering" will prove equally valuable in distributed computing on a cluster of workstations.
(source: Nielsen Book Data)

• Introduction Counter Propagation Neural Network in Structural Engineering Neural Dynamics Model for Structural Optimization - A Theory Application of the Neural Dynamics Model to the Plastic Design of Structures Nonlinear Neural Dynamics Model Optimization of Space Structures Hybrid CPN-Neural Dynamics Model for Discrete Optimization of Steel Structures Data Parallel Neural Dynamics Model for Design Optimization for Integrated Design of Large Steel Structures Distributed Neural Dynamics Algorithms for Optimization of Large Steel Structures References.
• (source: Nielsen Book Data)

"Neurocomputing for Design Automation" provides innovative design theories and computational models with two broad objectives: automation and optimization. This singular book: presents an introduction to the automation and optimization of engineering design of complex engineering systems using neural network computing; outlines new computational models and paradigms for automating the complex process of design for unique engineering systems, such as steel highrise building structures; applies design theories and models to the solution of structural design problems; and, integrates three computing paradigms such as mathematical optimization, neural network computing, and parallel processing.The applications described in this book are general enough to be applied directly or by extension to other engineering design problems, such as aerospace or mechanical design. Also, the computational models are shown to be stable and robust - particularly suitable for design automation of large systems, such as a 144-story steel super-highrise building structure with more than 20,000 members. The book provides an exceptional framework for the automation and optimization of engineering design, focusing on a new computing paradigm - neural networks computing. It presents the automation of complex systems at a new and higher level never achieved before.
(source: Nielsen Book Data)

• chapter 1 Dialectics of Nature: Inspiration for Computing
• chapter 2 Gravitational Search Algorithm
• chapter 3 Central Force Optimization
• chapter 4 Electromagnetism-Like Optimization
• chapter 5 Harmony Search
• chapter 6 Water Drop Algorithm
• chapter 7 Spiral Dynamics Algorithms
• chapter 8 Simulated Annealing
• chapter 9 Chemical Reaction Optimization
• chapter 10 Miscellaneous Algorithms

• 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)

• 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.
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• 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.
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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.
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• Contents History. Management. Evaluation. Preservation, Rehabilitation and Restoration.
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Explore Historic Bridge Design through the Perspective of Modern Engineering Historic Bridges:Evaluation, Preservation, and Management provides both an admiring and a technical account of bridge engineering through an exploration of several remarkable examples. From ancient China to modern-day Minnesota, the book discusses the history and structural evaluation of bridges, as well as their preservation, and restoration. With chapters written by renowned engineers, this unique resource - Compares the techniques and materials used in building three railroad bridges that traversed the Mississippi at the same site in 1865, 1887, and 1910 Investigates a legendary stone-arch bridge constructed in Ancient China in 606 A.D. Demonstrates how historians and engineers in Milwaukee found an approach to new bridge design that balances modern design standards with aesthetic interpretation Details a collaborative team approach to historic bridge management in Minnesota Considers the design and repair process of rapidly disappearing wrought iron bridges Discusses preservation of stone masonry aqueducts on the Chesapeake and Ohio Canal An educational treatise for engineers and historical preservationists, this work includes a wealth of illustrations and scientific tables. Demonstrating historic engineering significance beyond their utilitarian function, the bridges encountered in these pages are true landmarks, as worthy of emulation as they are preservation.
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• chapter 1 Introduction
• chapter 2 The Mississippi River Railway Crossing At Clinton, Iowa
• chapter 3 The Dragon Bridge Of Li Chun In Ancient China
• chapter 4 Bridging The Gap Connecting Design And Historic Preservation Goals On Milwaukee County's Historic Parkway Bridges
• chapter 5 Managing Historic Bridges In Minnesota The Historian And The Engineer Collaborate
• chapter 6 Structural Deck Evaluation Of The John A. Roebling Suspension Bridge
• chapter 7 Extant Lenticular Iron Truss Bridges From The Berlin Iron Bridge Company
• chapter 8 Wind And Truss Bridges Overview Of Research
• chapter 9 Mechanical Properties Of Wrought Iron From Penns Creek Bridge (1886)
• chapter 10 The Preservation Of Historic Bridges
• chapter 11 Preservation Of Historic Iron Bridges Adaptive Use Bridge Project, University Of Massachusettseamherst
• chapter 12 Preservation Of Stone Masonry Aqueducts On The Chesapeake And Ohio Canal
• chapter 13 Rehabilitation Of Two Historic Timber Covered Bridges In Massachusetts
• chapter 14 The Historic Rehabilitation Of The Market Street Bridge In Chattanooga, Tennessee
• chapter 15 Introduction

Summarizes advances in a number of fundamental areas of optimization, with application in engineering design. The main topics covered include: optimality criteria methods; model reduction and verification; mixed integer programming; and generalized geometric programming.
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The first volume in this new series has a companion in volume 2 (unseen), Parallel processing in computational mechanics . The first six contributions present general aspects of supercomputing from both hardware and software engineering points of view.
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• Representation of knowledge
• rule-based systems
• knowledge acquisition systems
• model-based knowledge acquisition
• models of expertise in knowledge engineering
• AI planning
• knowledge in the form of patterns and neural network computing
• machine learning
• propositional logic
• natural language processing.
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• Integrity constraints in knowledge-based systems
• symbolic decision procedures for knowledge-based systems
• applications of automated reasoning
• robot problem solving
• autonomous mobile robots
• AI techniques in computer-aided manufacturing systems
• AI techniques in software engineering
• knowledge-based vision systems
• automatic speech recognition
• knowledge processing and its application to engineering design.
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Designed as a text/reference for all areas of engineering where applied artificial intelligence is used. Each chapter presents a review and tutorial of a subject within the field of expert systems and knowledge engineering with the emphasis on state-of-art developments.
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The second volume of a two-volume series, addressing the application of knowledge engineering and expert systems technology to engineering problems.
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• Artificial intelligence and expert systems
• AI techniques and development of expert systems
• AI languages and programming environment
• expert systems shells
• an overview of expert systems in civil engineering
• expert systems in civil engineering
• expert systems for structural design
• expert systems applications in construction engineering
• knowledge engineering for a construction scheduling analysis system
• approximate reasoning in structural damage assessment
• condensation of knowledge base in expert systems with applications to seismic risk evaluation
• expert systems for condition evaluation of existing structures
• an expert system for earthquake intensity evaluation
• a knowledge-based approach to engineering information retrieval and management
• knowledge acquistion for expert systems in construction
• codes and rules and their roles as constraints in expert systems for structural design.
• (source: Nielsen Book Data)

Expert systems are interactive computer programs that simulate human expertise and decision-making processess. This authoritative book describes the application of artificial intelligence and expert systems technology in construction and structural engineering - the first book that deals with this important new application technology. The first four chapters introduce the reader to the field of artificial intelligence and expert system technology. Chapter five presents an overview of activities in the field of expert systems as applied to civil engineering problems. An international team of leading authorities have contributed chapters on their own specialities.
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This book contains sixteen papers presenting applications of expert system technology to civil engineering problems with emphasis on microcomputer implementations. It is divided into four parts: Structural engineering, geotechnical and environmental engineering, construction, and general. Topics include knowledge acquisition and machine learning, using PROLOG on a Macintosh, an environment for building integrated structural design expert systems, an integrated rule-based system for industrial building design, and integrating an expert system shell with spreadsheet programs. Expert systems for hazardous waste management, diagnosis and treatment of dam seepage problems, and analysis of activated sludge are presented. Also covered are knowledge elicitation techniques for construction scheduling, an expert system for construction contract claims, and knowledge acquisition for a contractor prequalification knowledge-based system. Finally, logic programming to manage constraint-based design, and development of an earthquake insurance and investment risk analysis system are discussed.
(source: Nielsen Book Data)