Neural and Adaptive Systems: Fundamentals Through Simulations

Front Cover
Wiley, 2000 - Computers - 656 pages

Develop New Insight into the Behavior of Adaptive Systems This one-of-a-kind interactive book and CD-ROM will help you develop a better understanding of the behavior of adaptive systems. Developed as part of a project aimed at innovating the teaching of adaptive systems in science and engineering, it unifies the concepts of neural networks and adaptive filters into a common framework. It begins by explaining the fundamentals of adaptive linear regression and builds on these concepts to explore pattern classification, function approximation, feature extraction, and time-series modeling/prediction. The text is integrated with the industry standard neural network/adaptive system simulator NeuroSolutions. This allows the authors to demonstrate and reinforce key concepts using over 200 interactive examples. Each of these examples is 'live,' allowing the user to change parameters and experiment first-hand with real-world adaptive systems. This creates a powerful environment for learning through both visualization and experimentation. Key Features of the Text

  • The text and CD combine to become an interactive learning tool.
  • Emphasis is on understanding the behavior of adaptive systems rather than mathematical derivations.
  • Each key concept is followed by an interactive example.
  • Over 200 fully functional simulations of adaptive systems are included.
  • The text and CD offer a unified view of neural networks, adaptive filters, pattern recognition, and support vector machines.
  • Hyperlinks allow instant access to keyword definitions, bibliographic references, equations, and advanced discussions of concepts.

The CD-ROM Contains:

  • A complete, electronic version of the text in hypertext format
  • NeuroSolutions, an industry standard, icon-based neural network/adaptive system simulator
  • A tutorial on how to use NeuroSolutions
  • Additional data files to use with the simulator

"An innovative approach to describing neurocomputing and adaptive learning systems from a perspective which unifies classical linear adaptive systems approaches with the modern advances in neural networks. It is rich in examples and practical insight."
—James Zeidler, University of California, San Diego

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Contents

DATA FITTING WITH LINEAR MODELS
1
CHAPTER
7
9
41
Copyright

22 other sections not shown

Common terms and phrases

adaptive systems Adatron algorithm anti-Hebbian applied associative memory Axon backpropagation bias Breadboard called Chapter classification coefficients competitive component compute convergence create criterion data clusters data set decision surface delay line delta rule desired response discriminant function dynamic eigenvalue eigenvectors equation error estimate Figure function approximation gamma memory Gaussian gradient descent Hebbian learning hidden layer hidden PEs Hopfield network implement impulse response input data input space iterations learning curve learning machine learning rate linear combiner linear regression linear system mapping matrix means method minimize minimum neural networks NEUROSOLUTIONS EXAMPLE Newton's method noise nonlinear normally one-hidden-layer MLP optimal orthogonal output parameters pattern perceptron performance surface problem projection quadratic recurrent regressor samples sine wave softmax solution solve square static step supervised learning Synapse TDNN TLFNs topology training set trajectory variable variance weight tracks weight update weight vector zero

Bibliographic information

TitleNeural and Adaptive Systems: Fundamentals Through Simulations
AuthorsJosé C. Principe, Neil R. Euliano, W. Curt Lefebvre
EditorW. Curt Lefebvre
Editionillustrated
PublisherWiley, 2000
ISBN0471351679, 9780471351672
Length656 pages
Subjects ›  › 

Computers / Data Science / Neural Networks
Technology & Engineering / Electrical
Technology & Engineering / Electronics / General
Technology & Engineering / Engineering (General)
  
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