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A Metaheuristic Approach to Two Dimensional Recursive Digital Filter Design

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Advances in Heuristic Signal Processing and Applications

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Abstract

The two dimensional IIR digital filter design problem has received increased attention over the past few years. Recently, several metaheuristic algorithms have been employed in this domain and have produced promising results. Invasive Weed Optimization is one of the latest population-based metaheuristic algorithms that mimics the colonizing action of weeds. In this chapter, an improvement to the classical weed optimization algorithm has been proposed by introducing a constriction factor in the seed dispersal phase. Temporal Difference Q-Learning has been employed to adapt this parameter for different population members through the successive generations. Such hybridization falls under a special class of adaptive Memetic Algorithms. The proposed memetic realization, called Intelligent Invasive Weed Optimization (IIWO), has been applied to the two-dimensional recursive digital filter design problem and it has outperformed several competitive algorithms that have been applied in this research field in the past.

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Authors and Affiliations

  1. Dept. of Electronics and Telecommunication Eng., Jadavpur University, Kolkata, India

    Abhronil Sengupta, Tathagata Chakraborti & Amit Konar

Authors
  1. Abhronil Sengupta
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  2. Tathagata Chakraborti
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  3. Amit Konar
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Correspondence to Abhronil Sengupta .

Editor information

Editors and Affiliations

  1. Electrical Engineering Department, Jadavpur University, Kolkata, 700032, West Bengal, India

    Amitava Chatterjee

  2. Dept. of Aerospace Engineering, Sharif University of Technology, Azadi Street, Tehran, Iran

    Hadi Nobahari

  3. Laboratory LiSSi, University of Paris 12, av. du Général de Gaulle 61, Créteil, 94010, France

    Patrick Siarry

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Sengupta, A., Chakraborti, T., Konar, A. (2013). A Metaheuristic Approach to Two Dimensional Recursive Digital Filter Design. In: Chatterjee, A., Nobahari, H., Siarry, P. (eds) Advances in Heuristic Signal Processing and Applications. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37880-5_8

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  • DOI: https://doi.org/10.1007/978-3-642-37880-5_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37879-9

  • Online ISBN: 978-3-642-37880-5

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