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GBF Trained Neuro-fuzzy Equalizer for Time Varying Channels

Author

Listed:
  • Archana Sarangi

    (Siksha O Anusandhan University, India)

  • Sasmita Kumari Padhy

    (Siksha O Anusandhan University, India)

  • Siba Prasada Panigrahi

    (Konark Institute of Science & Technology, India)

  • Shubhendu Kumar Sarangi

    (Siksha O Anusandhan University, India)

Abstract

This paper proposes a neuro-fuzzy filter for equalization of time-varying channels. Additionally, it proposes to tune the equalizer with a hybrid algorithm between Genetic Algorithms (GA) and Bacteria Foraging (BFO), termed as GBF. The major advantage of the method developed in this paper is that all parameters of the neuro-fuzzy network, including the rule base, are tuned simultaneously through the proposed hybrid algorithm of genetic Algorithm and bacteria foraging. The performance of the Neuro-Fuzzy equalizer designed using the proposed approach is compared with Genetic algorithm based equalizers. The results confirm that the methodology used in the paper is much better than existing approaches. The proposed hybrid algorithm also eliminates the limitations of GA based equalizer, i.e. the inherent characteristic of GA, i.e. GAs risk finding a sub-optimal solution.

Suggested Citation

  • Archana Sarangi & Sasmita Kumari Padhy & Siba Prasada Panigrahi & Shubhendu Kumar Sarangi, 2011. "GBF Trained Neuro-fuzzy Equalizer for Time Varying Channels," International Journal of Applied Evolutionary Computation (IJAEC), IGI Global, vol. 2(3), pages 27-38, July.
    • Handle: RePEc:igg:jaec00:v:2:y:2011:i:3:p:27-38
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