Feed-Forward Neural Networks: Vector Decomposition Analysis, Modelling and Analog Implementation - The Springer International Series in Engineering and Computer Science 314 (Paperback)Jouke Annema (author)
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Starting with the derivation of a specification and ending with its hardware implementation, analog hard-wired, feed-forward neural networks with on-chip back-propagation learning are designed in their entirety. On-chip learning is necessary in circumstances where fixed weight configurations cannot be used. It is also useful for the elimination of most mis-matches and parameter tolerances that occur in hard-wired neural network chips.
Fully analog neural networks have several advantages over other implementations: low chip area, low power consumption, and high speed operation.
Feed-Forward Neural Networks is an excellent source of reference and may be used as a text for advanced courses.
Publisher: Springer-Verlag New York Inc.
Number of pages: 238
Weight: 397 g
Dimensions: 235 x 155 x 14 mm
Edition: Softcover reprint of the original 1st ed. 199