Self-Tuning of a Neural Network Controller with an Integral Estimate of Contradictions between the Commands of the Learning Algorithm and Memory

Abstract

We propose an approach to organizing self-tuning for a controller based on an artificial neural network that uses information on the contradictions arising in the creation of the value for the control signal between accumulated memory of the neural network and the learning algorithm based on backpropagation. The activity of neural network memory is estimated as its reaction to changing the state of the control system. Self-tuning is done by controlling the learning rate coefficient with an integral controller in order to stabilize the integral criterion for estimating the contradictions. Based on this modeling, we show a conceptual possibility for the operation of the self-tuning system with constant tuning parameters in a wide range of changes of the control object’s dynamical properties.