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Erschienen in:
2009 | OriginalPaper | Buchkapitel
Gradient Like Behavior and High Gain Design of KWTA Neural Networks
verfasst von : Daniela Danciu, Vladimir Răsvan
Erschienen in: Bio-Inspired Systems: Computational and Ambient Intelligence
Verlag: Springer Berlin Heidelberg
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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.