Adaptation of Weights in a Neuron Using an Integrated Filter

Abstract

This paper presents a description of application of stochastic weights in a neuron, problem solved through the adaptive estimation achieved with dynamical combination between the identification and estimation; having an adaptive structure that updates the estimated parameters into the integrated filter. The weights are dynamically adjusted in the neuron based on stochastic gradient, affecting the neuronal performance allowing that its response converges to the reference signal. In addition, the error is applied in identification as an innovative gain adjusting the neuron in its inputs and consequently its dendrites signals that are applied into gradient filter adjusting the neuron weights in accordance with the desired signal requirement. Such that the gradient estimation is built based on the Black-box scheme with unknown internal weights. All simulations were developed using Matlab® software.