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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 Contents
[edit | edit source]Overview
[edit | edit source]
- Neural Network Basics
- Biological Neural Networks
- History
- MATLAB Neural Networking Toolbox
- Activation Functions
ANN Models
[edit | edit source]- 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
- Autoencoders
- Convolutional Neural Networks
Teaching and Learning
[edit | edit source]- Learning Paradigms
- Error-Correction Learning
- Hebbian Learning
- Competitive Learning
- Boltzmann Learning
- ART Learning
- Self-Organizing Maps
Applications
[edit | edit source]- Pattern Recognition
- Clustering
- Feature Detection
- Series Prediction
- Data Compression
- Curve Fitting
- Optimization
- Control