A fast on-line neural-network training algorithm for a rectifier regulator

Abstract

This paper addresses the problem of deadbeat control in fully controlled high-power factor rectifiers. Improved deadbeat control can be achieved through the use of neural network-based predictors for the input current reference to the rectifier. In this application, online training is absolutely required. In order to achieve sufficiently fast online training, a new random search algorithm is presented and evaluated. Simulation results show that this type of network training yields equivalent performance to standard backpropagation training. Unlike backpropagation, however, the random weight change method can be implemented in mixed digital/analog hardware for this application. The paper proposes a very large-scale integration implementation which achieves a training epoch as low as 8 /spl mu/s.