PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Adaptive Sigmoidal Plant Identification
Angel Navia-Vazquez and R. Díaz-Morales
Signal Processing Volume 91, Number 1, pp. 1066-1070, 2011. ISSN 0165-1684

Abstract

Logistic models, comprising a linear filter followed by a nonlinear memoryless sigmoidal function, are often found in practice in many fields, e.g., biology, probability modelling, risk prediction, forecasting, signal processing, electronics and communications, etc., and in many situations a real time response is needed. The online algorithms used to update the filter coefficients usually rely on gradient descent (e.g., nonlinear counterparts of the Least Mean Squares algorithm). Other algorithms, such as Recursive Least Squares, although promising improved characteristics, cannot be directly used due to the nonlinearity in the model. We propose here a modified Recursive Least Squares algorithm that provides better performance than competing state of the art methods in an adaptive sigmoidal plant identification scenario.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:7523
Deposited By:Angel Navia-Vazquez
Deposited On:17 March 2011