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 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.
Salam
F A
FA
Dept. of Electr. Eng., Michigan State Univ., East Lansing, MI.
Bai
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