A new feedback neural network with supervised learning

A model is introduced for continuous-time dynamic feedback neural networks with supervised learning ability. Modifications are introduced to conventional models to guarantee precisely that a given desired vector, and its negative, are indeed stored in the network as asymptotically stable equilibrium...

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Bibliographic Details
Published in:IEEE transactions on neural networks 1991-01, Vol.2 (1), p.170-173
Main Authors: Salam, F.M.A., Bai, S.
Format: Article
Language:English
Subjects:
Artificial neural networks
Biological system modeling
Chaos
Exact sciences and technology
Feedforward neural networks
Function theory, analysis
Mathematical methods in physics
Neural networks
Neurofeedback
Neurons
Physics
Stability
State feedback
Supervised learning
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Summary:A model is introduced for continuous-time dynamic feedback neural networks with supervised learning ability. Modifications are introduced to conventional models to guarantee precisely that a given desired vector, and its negative, are indeed stored in the network as asymptotically stable equilibrium points. The modifications entail that the output signal of a neuron is multiplied by the square of its associated weight to supply the signal to an input of another neuron. A simulation of the complete dynamics is then presented for a prototype one neuron with self-feedback and supervised learning; the simulation illustrates the (supervised) learning capability of the network.< >
ISSN:1045-9227
1941-0093
DOI:10.1109/72.80309