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Digital IIR Filter Design with Fix-Point Representation Using Effective Evolutionary Local Search Enhanced Differential Evolution

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Computational Intelligence in Digital and Network Designs and Applications

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Abstract

Previously, the parameters of digital IIR filters were encoded with floating-point representations. It is known that a fixed-point representation can effectively save computational resources and is more convenient for direct realization on hardware. Inherently, compared with floating-point representation, fixed-point representation may make the search space miss much useful gradient information and, therefore, raises new challenges. In this chapter, the universality of DE-based MA is improved by implementing more efficient evolutionary algorithms (EAs) as the local search techniques. The performance of the newly designed algorithm is experimentally verified in both function optimization tasks and digital IIR filter design problems.

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Authors and Affiliations

  1. Alibaba Group, Hangzhou, China

    Yu Wang

  2. IBM Research-China, Beijing, China

    Yu Wang, Weishan Dong, Junchi Yan, Li Li, Chunhua Tian, Chao Zhang, Zhihu Wang & Chunyang Ma

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  1. Yu Wang
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  1. Department of Electronics, ENET’Com, University of Sfax, Sfax, Tunisia

    Mourad Fakhfakh

  2. Department of Electronics, INAOE, Tonantzintla, Mexico

    Esteban Tlelo-Cuautle

  3. Laboratoire LiSSi (EA 3956), Université Paris-Est Créteil, Vitry sur Seine, France

    Patrick Siarry

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Wang, Y. et al. (2015). Digital IIR Filter Design with Fix-Point Representation Using Effective Evolutionary Local Search Enhanced Differential Evolution. In: Fakhfakh, M., Tlelo-Cuautle, E., Siarry, P. (eds) Computational Intelligence in Digital and Network Designs and Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-20071-2_5

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