Neurocontrol in Sequence Recognition

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“Neurocontrol in Sequence Recognition” by W. Byrne and S. Shamma. In Progress in Neural Networks: Neural Networks for Control, (O. Omidvar and D. Elliott, eds.), 1997, pp. 31-56 (26 pages).

Abstract

An artificial neural network intended for sequence modeling and recognition is described. The network is based on a lateral inhibitory network with controlled, oscillatory behavior so that it naturally models sequence generation. Dynamic programming algorithms can be used to transform the network into a sequence recognizer. Markov decision theory is used to develop novel and more “neural” recognition control strategies as alternatives to dynamic programming.

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BibTeX entry:

@incollection{ncsr97,
   author = {W. Byrne and S. Shamma},
   editor = {O. Omidvar and D. Elliott},
   title = {Neurocontrol in Sequence Recognition},
   booktitle = {Progress in Neural Networks: Neural Networks for Control},
   pages = {31--56 (26 pages)},
   publisher = {Academic Press},
   year = {1997}
}

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