Description

Book Synopsis
Like 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 Contents

Chapter 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

Neural and Adaptive Systems

    Product form

    A Paperback / softback by José C. Principe, Neil R. Euliano, W. Curt Lefebvre

    £129.15

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      Book details

      Publisher John Wiley & Sons Inc
      Published 13 January 2000
      ISBN-13 9780471351672
      978-0471351672
      ISBN-10 0471351679
      Also in:
      Neural networks and fuzzy systems

      Description

      Book Synopsis
      Like 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 Contents

      Chapter 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

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