DocumentCode :
867182
Title :
Nonlinear process identification using decision theory
Author :
Miller, R.W. ; Roy, R.
Author_Institution :
Cornell Aeronautical Lab., Buffalo, NY, USA
Volume :
9
Issue :
4
fYear :
1964
fDate :
10/1/1964 12:00:00 AM
Firstpage :
538
Lastpage :
540
Abstract :
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.
Keywords :
Decision making; Nonlinear systems; Process control; System identification; Computer vision; Decision theory; Equations; History; Indexing; Pattern recognition; Probability distribution; Process control; Quantization; Sampling methods;
fLanguage :
English
Journal_Title :
Automatic Control, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9286
Type :
jour
DOI :
10.1109/TAC.1964.1105767
Filename :
1105767
Link To Document :
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