Neural network architecture for slow nonlinear time-delayed processes
A.J.P. de Carvalho, A.M. Campos · 2005
An experimental multilayered neural network architecture for the control of a given plant is presented. It will be implemented using multi-microcomputing techniques, based on synchronized parallel processing, using associative shared memory message passing, and the retrieval of knowledge of plant and controller parameters. This new concept of control is used to solve the difficulties present in the digital control of slow nonlinear time-delayed processes, especially the case of systems with long dead times. This paper explains how the Smith predictor algorithm was modified in order to adapt it to a multilayered neural network architecture.