Neural and Adaptive Systems: Fundamentals through Simulations

Data Fitting with Linear Models.

Pattern Recognition.

Multilayer Perceptrons.

Designing and Training MLPs.

Function Approximation with MLPs, Radial Basis Functions, and Support Vector Machines.

Hebbian Learning and Principal Component Analysis.

Competitive and Kohonen Networks.

Principles of Digital Signal Processing.

Adaptive Filters.

Temporal Processing with Neural Networks.

Training and Using Recurrent Networks.

Appendices.

Glossary.

Index.