Description
Book SynopsisLike no other text in this field, authors Jose C. Principe, Neil R. Euliano, and W. Curt Lefebvre have written a unique and innovative text unifying the concepts of neural networks and adaptive filters into a common framework.
The text is suitable for senior/graduate courses in neural networks and adaptive filters. It offers over 200 fully functional simulations (with instructions) to demonstrate and reinforce key concepts and help the reader develop an intuition about the behavior of adaptive systems with real data. This creates a powerful self-learning environment highly suitable for the professional audience.
Table of ContentsChapter 1 Data Fitting with Linear Models 1
Chapter 2 Pattern Recognition 68
Chapter 3 Multilayer Perceptrons 100
Chapter 4 Designing and Training MLPS 173
Chapter 5 Function Approximation with MLPs, Radial Basis Functions, and Support Vector Machines 223
Chapter 6 Hebbian Learning and Principal Component Analysis 279
Chapter 7 Competitive and Kohonen Networks 333
Chapter 8 Principles of Digital Signal Processing 364
Chapter 9 Adaptive Filters 429
Chapter 10 Temporal Processing with Neural Networks 473
Chapter 11 Training and Using Recurrent Networks 525
Appendix A Elements of Linear Algebra and Pattern Recognition 589
Appendix B NeuroSolutions Tutorial 613
Appendix C Data Directory 637
Glossary 639
Index 647
