Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms: Industrial Applications

Preface
Chapter 1—Introduction to Neural Networks, Fuzzy Systems, Genetic
Algorithms, and their Fusion
1. Knowledge-Based Information Systems
2. Artificial Neural Networks
3. Evolutionary Computing
4. Fuzzy Logic
5. Fusion
6. Summary
References
Chapter 2—A New Fuzzy-Neural Controller
1. Introduction
2. RBF Based Fuzzy System with Unsupervised Learning
2.1 Fuzzy System Based on RBF
2.2 Coding
2.3 Selection
2.4 Crossover Operator
2.5 Mutation Operator
3. Hierarchical Fuzzy-Neuro Controller Based on Skill Knowledge Database
4. Fuzzy-Neuro Controller for Cart-Pole System
5. Conclusions
References
Chapter 3—Expert Knowledge-Based Direct Frequency Converter Using
Fuzzy Logic Control
1. Introduction
2. XDFC Topology and Operation
3. Space Vector Model of the DFC
4. Expert Knowledge-Based SVM
5. XDFC Control
5.1 XDFC Control Strategy and Operation
5.2 Fuzzy Logic Controller
5.3 Load’s Line Current Control
5.4 Input’s Line Current Control
6. Results
7. Evaluation
8. Conclusion
References
Chapter 4—Design of an Electro-Hydraulic System Using Neuro-Fuzzy
Techniques
1. Introduction
2. The Fuzzy Logic System
2.1 Fuzzification
2.2 Inference Mechanism
2.3 Defuzzification
3. Fuzzy Modeling
4. The Learning Mechanism
4.1 Model Initialization
4.2 The Cluster-Based Algorithm
4.3 Illustrative Example
4.4 The Neuro-Fuzzy Algorithm
5. The Experimental System
5.1 Training Data Generation
6. Neuro-Fuzzy Modeling of the Electro-Hydraulic Actuator
7. The Neuro-Fuzzy Control System
7.1 Experimental Results
8. Conclusion
References
Chapter 5—Neural Fuzzy Based Intelligent Systems and Applications
1. Introduction
2. Advantages and Disadvantages of Fuzzy Logic and Neural Nets
2.1 Advantages of Fuzzy Logic
2.2 Disadvantages of Fuzzy Logic
2.3 Advantages of Neural Nets2.4 Disadvantages of Neural Nets
3. Capabilities of Neural Fuzzy Systems (NFS)
4. Types of Neural Fuzzy Systems
5. Descriptions of a Few Neural Fuzzy Systems
5.1 NeuFuz
5.1.1 Brief Overview
5.1.2 NeuFuz Architecture
5.1.3 Fuzzy Logic Processing
5.2 Recurrent Neural Fuzzy System (RNFS)
5.2.1 Recurrent Neural Net
5.2.2 Temporal Information and Weight Update
5.2.3 Recurrent Fuzzy Logic
5.2.4 Determining the Number of Time Delays
6. Representative Applications
6.1 Motor Control
6.1.1 Choosing the Inputs and Outputs
6.1.2 Data Collection and Training
6.1.3 Rule Evaluation and Optimization
6.1.4 Results and Comparison with the PID Approach
6.2 Toaster Control
6.3 Speech Recognition using RNFS
6.3.1 Small Vocabulary Word Recognition
6.3.2 Training and Testing
7. Conclusion
References
Chapter 6—Vehicle Routing through Simulation of Natural Processes
1. Introduction
2. Vehicle Routing Problems
3. Neural Networks
3.1 Self-Organizing Maps
3.1.1 Vehicle Routing Applications
3.1.2 The Hierarchical Deformable Net
3.2 Feedforward Models
3.2.1 Dynamic vehicle routing and dispatching
3.2.2 Feedforward Neural Network Model with Backpropagation
3.2.3 An Application for a Courier Service
4. Genetic Algorithms
4.1 Genetic clustering
4.1.1 Genetic Sectoring (GenSect)
4.1.2 Genetic Clustering with Geometric Shapes (GenClust)
4.1.3 Real-World Applications
4.2 Decoders4.3 A Nonstandard GA
5. Conclusion
Acknowledgments
References
Chapter 7—Fuzzy Logic and Neural Networks in Fault Detection
1. Introduction
2. Fault Diagnosis
2.1 Concept of Fault Diagnosis
2.2 Different Approaches for Residual Generation and Residual Evaluation
3. Fuzzy Logic in Fault Detection
3.1 A Fuzzy Filter for Residual Evaluation
3.1.1 Structure of the Fuzzy Filter
3.1.2 Supporting Algorithm for the Design of the Fuzzy Filter
3.2 Application of the Fuzzy Filter to a Wastewater Plant
3.2.1 Description of the Process
3.2.2 Design of the Fuzzy Filter for Residual Evaluation
3.2.3 Simulation Results
4. Neural Networks in Fault Detection
4.1 Neural Networks for Residual Generation
4.1.1 Radial-Basis-Function(RBF) Neural Networks
4.1.2 Recurrent Neural Networks (RNN)
4.2 Neural Networks for Residual Evaluation
4.2.1 Restricted-Coulomb-Energy (RCE) Neural Networks
4.3 Application to the Industrial Actuator Benchmark Test
4.3.1 Simulation Results for Residual Generation
4.3.2 Simulation Results for Residual Evaluation
5. Conclusions
References
Chapter 8—Application of the Neural Network and Fuzzy Logic to the
Rotating Machine Diagnosis
1. Introduction
2. Rotating Machine Diagnosis
2.1 Fault Diagnosis Technique for Rotating Machines
3. Application of Neural Networks and Fuzzy Logic for Rotating Machine Diagnosis
3.1 Fault Diagnosis Using a Neural Network
3.2 Fault Diagnosis Using Fuzzy Logic
4. Conclusion
References
Chapter 9—Fuzzy Expert Systems in ATM Networks
1. Introduction

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