Nonlinear process identification using decision theory

This paper presents a learning technique for obtaining a model of a finite memory nonlinear process using only the input-output operating record. The model obtained simulates the process cause-effect relationship rather than the detailed structure of the process. As such, it is a "black box" model which can be used as a fast-time model for least-time control of the process. The learning technique used is similar to the technique of feature detection used in pattern recognition. Certain features of the input waveform are observed, along with the quantized output levels . From these observations the lower-order probability distributions are obtained. These lower-order probability distributions are used to approximate the higher-order distributions . By incorporating these higher-order distributions into the equations of decision theory, the process output for a given input can be obtained.