Local Adaptive Nonlinear Filter Prediction Model with a Parameter for Chaotic Time Series

Abstract:

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In order to improve the predictive performance for chaotic time series, we propose a novel local adaptive nonlinear filter prediction model. We use a function with a parameter to build an adaptive nonlinear filter in this model, and we train this model with an adaptive algorithm, deduced by the minimum square-root-error criterion and the steepest gradient descent rule. We evaluate the proposed model using four well-known chaotic systems, namely Logistic map, Henon map, Lorenz system and Rosslor system. All the results show a remarkable increase in predictive performance, comparing with the local adaptive nonlinear filter prediction model.

Info:

Periodical:

Edited by:

Ran Chen

Pages:

3180-3184

DOI:

10.4028/www.scientific.net/AMM.44-47.3180

Citation:

F. Fang et al., "Local Adaptive Nonlinear Filter Prediction Model with a Parameter for Chaotic Time Series", Applied Mechanics and Materials, Vols. 44-47, pp. 3180-3184, 2011

Online since:

December 2010

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Price:

$35.00

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