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A stochastic approximation, or stochastic gradient procedure, is a method of estimating the minimum of a criterion function despite the random effects of noise. It was originally conceived by the mathematician as a means of estimating the minimum of a regression function. In recent years, however, stochastic approximation has received considerable attention in the engineering field because of its potential application to learning systems, parameter estimation, and automatic control.
P.C. Young, "R70-8 Analog Methods for On-Line System Identification Using Noisy Measurements", IEEE Transactions on Computers, vol. 19, no. , pp. 465-466, May 1970, doi:10.1109/T-C.1970.222959
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