A feedback neural network with supervised learning

A model for continuous-time dynamic feedback neural networks with supervised learning ability is introduced. Modifications were introduced to conventional models to guarantee analytically 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 presented for a prototype one neuron with self-feedback and supervised learning. The simulation illustrates the (supervised) learning capability of the network