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- Preface
- Organization
- Acknowledgments
- Contents
- About the Authors
- Architectures of Artificial Neural Networks and Their Theoretical Aspects
- 1 Introduction
- 2 Artificial Neural Network Architectures and Training Processes
- 3 The Perceptron Network
- 4 The ADALINE Network and Delta Rule
- 5 Multilayer Perceptron Networks
- 5.1 Introduction
- 5.2 Operating Principle of the Multilayer Perceptron
- 5.3 Training Process of the Multilayer Perceptron
- 5.4 Multilayer Perceptron Applications
- 5.5 Aspects of Topological Specifications for MLP Networks
- 5.6 Implementation Aspects of Multilayer Perceptron Networks
- 5.7 Exercises
- 5.8 Practical Work 1 (Function Approximation)
- 5.9 Practical Work 2 (Pattern Classification)
- 5.10 Practical Work 3 (Time-Variant Systems)
- 6 Radial Basis Function Networks
- 7 Recurrent Hopfield Networks
- 8 Self-Organizing Kohonen Networks
- 9 LVQ and Counter-Propagation Networks
- 10 ART (Adaptive Resonance Theory) Networks
- Application of Artificial Neural Networks in Engineering and Applied Science Problems
- 11 Coffee Global Quality Estimation Using Multilayer Perceptron
- 12 Computer Network Traffic Analysis Using SNMP Protocol and LVQ Networks
- 13 Forecast of Stock Market Trends Using Recurrent Networks
- 14 Disease Diagnostic System Using ART Networks
- 15 Pattern Identification of Adulterants in Coffee Powder Using Kohonen Self-organizing Map
- 16 Recognition of Disturbances Related to Electric Power Quality Using MLP Networks
- 17 Trajectory Control of Mobile Robot Using Fuzzy Systems and MLP Networks
- 18 Method for Classifying Tomatoes Using Computer Vision and MLP Networks
- 19 Performance Analysis of RBF and MLP Networks in Pattern Classification
- 20 Solution of Constrained Optimization Problems Using Hopfield Networks
- Appendix A
- Appendix B
- Appendix C
- Appendix D
- Appendix E
- References
- Index
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Artificial Neural Networks：A Practical Course
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