NDLI: Exact expectation analysis of the LMS adaptive identification of non-linear systems

Content Provider	IET Digital Library
Author	Lara, P. Igreja, F. Silva, T. T. P. Tarrataca, L. D. T. J. Haddad, D. B.
Abstract	Most adaptive filtering schemes employ the tapped-delay line. In part, such a fact can be explained by the assumption that the plant they intend to estimate is linear. Although such a hypothesis can be reasonable if the input signal is constrained to a certain range, sometimes it may not be valid. In this last case, the performance and stability guarantees provided by stochastic models that presume linearity of the ideal system are no longer valid. This Letter advances an analytic model of the least-mean-square (LMS) learning capabilities when the ideal system is not linear, with the additive noise including non-linear functions of the input samples. The proposed analysis does not assume neither that the excitation signal is statistically independent of the adaptive weights, nor that the additive noise is white and/or independent of input data. Furthermore, it can be applied to non-Gaussian and/or non-white input signals. Simulations show that the advanced model is more accurate than traditional approaches.
Starting Page	45
Ending Page	48
Page Count	4
ISSN	00135194
Volume Number	56
e-ISSN	1350911X
Issue Number	Issue 1, Jan (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/56/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2019.3096
Journal	Electronics Letters
Publisher Date	2019-10-24
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Filter Adaptive Weights Additive Noise Including Nonlinear Function Analytic Model Exact Expectation Analysis Excitation Signal Filtering Method in Signal Processing Filtering Scheme Ideal System Input Signal Interpolation And Function Approximation Least Mean Squares Method Least-mean-square Learning Capabilities Letter Advances Linear System LMS Adaptive Identification Noise Nonlinear System Nonwhite Input Signal Numerical Analysis Signal Processing Theory Stability Guarantees Statistics Stochastic Linearised SCUC Stochastic Model Tapped-delay Line
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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