Weak convergence results for sequential regression in memoryless systems†
作者:
JOSEPH PERL,
期刊:
International Journal of Systems Science
(Taylor Available online 1977)
卷期:
Volume 8,
issue 11
页码: 1243-1247
ISSN:0020-7721
年代: 1977
DOI:10.1080/00207727708942118
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
Weak convergence results are obtained for a sequential regression algorithm that arises in the identification of nonlinear, memoryless systems and the adaptive design of moving average filters. The algorithm is shown to be weakly consistent if the system input is a wide-sense stationary sequence of order four that satisfies certain covariance and fourth-cumulant conditions. The conditions are essentially asymptotic independence requirements that permit one to relax the (usually required) strict independence requirements on the input data.
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