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Nonlinear process identification using decision theory

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2 Author(s)
R. Miller ; Cornell Aeronautical Lab., Buffalo, NY, USA ; R. Roy

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 \alpha _{1}, \alpha _{2}, ... , \alpha _{N} are observed, along with the quantized output levels y_{1}, y_{2}, ... , y_{m} . From these observations the lower-order probability distributions P[\alpha _{j}/y_{i}] are obtained. These lower-order probability distributions are used to approximate the higher-order distributions P(\alpha _{1}, \alpha _{2}, ... , \alpha _{N}, y_{i}) . By incorporating these higher-order distributions into the equations of decision theory, the process output for a given input can be obtained.

Published in:

IEEE Transactions on Automatic Control  (Volume:9 ,  Issue: 4 )