Cover
Half Title Page
Title page
Copyright Page
About the Author
Table of Contents
List of Figures
List of Tables
Preface
Chapter 1 Introduction
1.1. Differences Between The Brain and A Computer
1.2. Artificial Neural Networks
1.3. Definition and Characteristics
1.4. Processing Stages
1.5. Training or Learning
Chapter 2 Application of an Intelligent Hopfield Neural Networks For Face Recognition
2.1. Methods And Techniques In Face Recognition Of Digital Images
2.2. Face Recognition Using Artificial Neural Networks
2.3. Feature Extraction Techniques
2.4. Pattern Recognition 2.5. Association and Classification2.6. Natural Language Processing
2.7. Network Layer: Perceptron, Adaline, And Madaline
2.8. Backpropagation
2.9. Validation
Chapter 3 Artificial Neural Networks
3.1. Introduction
3.2. Analogy With The Brain
3.3. Neural Networks
3.4. Network Operation
3.5. Operation of The Layers
3.6. What Makes The Different Neurocomputation
3.7. Pattern Recognition
3.8. Power Synthesis
3.9. Frank Rosenblatt's Perceptron
3.10. Backpropagation
Chapter 4 Neural Networks Applied to the Analysis of Images
4.1. Introduction To Patterns In Image Recognition 4.2. Digital Images4.3. Applying Neural Networks
Chapter 5 Image Analysis System
5.1. System Structure
5.2. Analysis of The Image
5.3. Architecture
5.4. Image Processing
5.5. Training Process
Chapter 6 Design and Construction of A System For Detecting Electromyographic Signals Using Neural Networks
6.1. Electrodes
6.2. Electromyography
6.3. Electronic Fundamentals
6.4. The Electromyograph
6.5. Design And Construction Of The Prototype For The Acquisition of Electromyographic Signals With Bipolar Source
Chapter 7 Conclusions
Bibliography
Index