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Principal Component Neural Networks: Theory and Applications |
| Adaptive and Learning Systems for Signal Processing, Communications and Control Series |
| K. I. Diamantaras (Aristotle Univ., Thessaloniki, Greece); S. Y. Kung (Princeton Univ.) |
| Systematically explores the relationship between principal component analysis (PCA) and neural networks. Provides a synergistic examination of the mathematical, algorithmic, application and architectural aspects of principal component neural networks. Using a unified formulation, the authors present neural models performing PCA from the Hebbian learning rule and those which use least squares learning rules such as back-propagation. Examines the principles of biological perceptual systems to explain how the brain works. Every chapter contains a selected list of applications examples from diverse areas.
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| Cloth Bound |
272 Pages, 6-1/8 x 9-1/4 in.
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Item #:
Price: |
0471054364
$145.00 |
John Wiley & Sons, Inc. | |
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