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Signal Analysis and Prediction pp 263–274Cite as

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Self-Adaptive Evolution Strategies for the Adaptation of Non-Linear Predictors in Time Series Analysis

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Part of the book series: Applied and Numerical Harmonic Analysis ((ANHA))

Abstract

The application of evolutionary computation techniques to the prediction of nonlinear and non-stationary stochastic signals is presented — a task that arises, e.g., in time series analysis. Especially, the online adaptation of bilinear predictors with the help of a multi-membered (μ, λ) — evolution strategy with self-adaptation of strategy parameters is treated. Special emphasis is given to the tracking capabilities of this specific evolutionary algorithm in non-stationary environments. The novel modifications of the standard (μ, λ) — evolution strategy are detailed that are necessary to obtain a computationally efficient algorithm. Using the evolutionary adapted bilinear predictor as part of a bilinear prediction error filter, the proposed methodology is applied to estimating bilinear stochastic signal models. Experimental results are given that demonstrate the robustness and efficiency of the (μ, λ) — evolution strategy in this digital signal processing application.

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Author information

Authors and Affiliations

  1. Siemens AG, Microelectronics Design Center, Wacholderstraße 7, 40489, Düsseldorf, Germany

    André Neubauer

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  1. André Neubauer
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Editor information

Editors and Affiliations

  1. Institute of Chemical Technology, Prague, Czech Republic

    Ales Procházka

  2. Czech Technical University, Prague, Czech Republic

    Jan Uhlíř

  3. University of Cambridge, England, UK

    P. W. J. Rayner  & N. G. Kingsbury  & 

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© 1998 Springer Science+Business Media New York

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Neubauer, A. (1998). Self-Adaptive Evolution Strategies for the Adaptation of Non-Linear Predictors in Time Series Analysis. In: Procházka, A., Uhlíř, J., Rayner, P.W.J., Kingsbury, N.G. (eds) Signal Analysis and Prediction. Applied and Numerical Harmonic Analysis. Birkhäuser, Boston, MA. https://doi.org/10.1007/978-1-4612-1768-8_19

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  • DOI: https://doi.org/10.1007/978-1-4612-1768-8_19

  • Publisher Name: Birkhäuser, Boston, MA

  • Print ISBN: 978-1-4612-7273-1

  • Online ISBN: 978-1-4612-1768-8

  • eBook Packages: Springer Book Archive

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