Foreword. 1. Introduction. 2. The Vector Decomposition Method. 3. Dynamics of Single Layer Nets. 4. Unipolar Input Signals in Single-Layer Feed-Forward Neural Networks. 5. Cross-Talk in Single-Layer Feed-Forward Neural Networks. 6. Precision Requirement for Analog Weight Adaptation Circuitry for Single-Layer Nets. 7. Discretization of Weight Adaptations in Single-Layer Nets. 8. Learning Behavior and Temporary Minima of Two-Layer Neural Networks. 9. Biases and Unipolar Input Signals for Two-Layer Neural Networks. 10. Cost Functions for Two-Layer Neural Networks. 11. Some Issues for f'(x). 12. Feed-Forward Hardware. 13. Analog Weight Adaptation Hardware. 14. Conclusions. Index. Nomenclature.
Feed-Forward Neural Networks: Vector Decomposition Analysis, Modelling and Analog Implementation presents a novel method for the mathematical analysis of neural networks that learn according to the back-propagation algorithm. The book also discusses some other recent alternative algorithms for hardware implemented perception-like neural networks. The method permits a simple analysis of the learning behaviour of neural networks, allowing specifications for their building blocks to be readily obtained.
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.
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