The addition of artificial network computing to traditional pattern recognition has given rise to a new, different, and more powerful methodology that is presented in this practical guide to the application of artificial neural networks. The material covered in the book is accessible to working engineers with little or no explicit background in neural networks.
The addition of artificial network computing to traditional pattern recognition has given rise to a new, different, and more powerful methodology that is presented in this practical guide to the application of artificial neural networks. The material covered in the book is accessible to working engineers with little or no explicit background in neural networks.
1. Introduction 2. Neural Networks: An Overview 3. Preprocessing 4. Feed-Forward Networks with Supervised Learning 5. Some Other Types of Neural Networks 6. Feature Extraction I: Geometric Features and Transformations 7. Feature Extraction II: Principal Component Analysis 8. Kohonen Networks and Learning Vector Quantization 9. Neural Associative Memories and Hopfield Networks 10. Adaptive Resonance Theory (ART) 11. Neocognitron 12. Systems with Multiple Classifiers
1. Introduction 2. Neural Networks: An Overview 3. Preprocessing 4. Feed-Forward Networks with Supervised Learning 5. Some Other Types of Neural Networks 6. Feature Extraction I: Geometric Features and Transformations 7. Feature Extraction II: Principal Component Analysis 8. Kohonen Networks and Learning Vector Quantization 9. Neural Associative Memories and Hopfield Networks 10. Adaptive Resonance Theory (ART) 11. Neocognitron 12. Systems with Multiple Classifiers
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