Title: A class of unbiased identification for inverse system with input noises
Abstract: In identifying the inverse system, the input is the output from the original system. This signal is corrupted by noises with unknown variance. When the ordinary least-squares method is applied to estimate the parameters of the inverse system, the estimates turn out to be biased. A new identification algorithm for bias compensation is proposed. Therein, the noise variance of the inverse system input is first estimated using the wavelet transform, and then, a recursive least-squares method with bias-elimination is used to estimate the parameters of the inverse system. Thus, the proposed algorithm does not require the input signal to be the white noise with a zero mean. Since the computation is recursive, it can be implemented online for estimating parameters of the inverse system. Experimental results show that the approach is effective.
Publication Year: 2009
Publication Date: 2009-01-01
Language: en
Type: article
Access and Citation
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot