Gradient like behavior and high gain design of KWTA neural networks
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 0302-9743, Vol: 5517 LNCS, Issue: PART 1, Page: 24-32
2009
- 12Citations
- 4Captures
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Conference Paper Description
It is considered the static and dynamic analysis of an analog electrical circuit having the structure of the Hopfield neural network, the KWTA (K-Winners-Take-All) network. The mathematics of circuit design and operation is discussed via two basic tools: the Liapunov function ensuring the gradient like behavior and the rational choice of the weights that stands for network training to ensure order-preserving trajectories. Dynamics and behavior at equilibria are considered in their natural interaction, and some connections to the ideas in general dynamical systems of convolution type are suggested. © 2009 Springer Berlin Heidelberg.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=68749099998&origin=inward; http://dx.doi.org/10.1007/978-3-642-02478-8_4; http://link.springer.com/10.1007/978-3-642-02478-8_4; http://link.springer.com/content/pdf/10.1007/978-3-642-02478-8_4; https://dx.doi.org/10.1007/978-3-642-02478-8_4; https://link.springer.com/chapter/10.1007/978-3-642-02478-8_4
Springer Science and Business Media LLC
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