Artificial neural networks are a computational tool, based on the properties of biological neural systems. Neural networks excel in a number of problem areas where conventional von Neumann computer systems have traditionally been slow and inefficient. This book is going to discuss the creation and use of artificial neural networks.

## Table of ContentsEdit

### OverviewEdit

- Neural Network Basics
- Biological Neural Networks
- History
- MATLAB Neural Networking Toolbox
- Activation Functions

### ANN ModelsEdit

- Feed-Forward Networks
- Radial Basis Function Networks
- Recurrent Networks
- Echo State Networks
- Hopfield Networks
- Self-Organizing Maps
- Competitive Models
- ART Models
- Boltzmann Machines
- Committee of Machines

### Teaching and LearningEdit

- Learning Paradigms
- Error-Correction Learning
- Hebbian Learning
- Competitive Learning
- Boltzmann Learning
- ART Learning
- Self-Organizing Maps

### ApplicationsEdit

- Pattern Recognition
- Clustering
- Feature Detection
- Series Prediction
- Data Compression
- Curve Fitting
- Optimization
- Control

### Future WorkEdit

- Criticisms and Problems
- Artificial Intelligence