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Quantum Particle Swarm Optimization Tuned Artificial Neural Network Equalizer

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Soft Computing: Theories and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 583))

Abstract

This article uses Artificial Neural Network (ANN) trained with Quantum behaved Particle Swarm Optimization (QPSO) for the problem of equalization. Though the use of PSO in training of ANN finds optimal weights of the network it fails in the design of appropriate topology. But, QPSO is capable of optimizing the network topology. Here, parameters like neurons in each layer, the number of layers etc. are optimized using QPSO. Then, this QPSO tuned ANN then applied to the problem of channel equalization. The superior performance of proposed equalizer is proved through simulations.

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Author information

Authors and Affiliations

  1. DRIEMS, Cuttack, India

    Gyanesh Das

  2. ECE, GITAM University, Bangalore, Karnataka, India

    Sunita Panda

  3. VSSUT, Burla, Odisha, India

    Sasmita Kumari Padhy

Authors
  1. Gyanesh Das
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  2. Sunita Panda
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  3. Sasmita Kumari Padhy
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Corresponding author

Correspondence to Sasmita Kumari Padhy .

Editor information

Editors and Affiliations

  1. Department of Applied Science and Engineering, IIT Roorkee, Saharanpur, India

    Millie Pant

  2. Department of Physics, Amity School of Applied Sciences, Amity University Rajasthan, Jaipur, Rajasthan, India

    Kanad Ray

  3. Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University Rajasthan, Jaipur, Rajasthan, India

    Tarun K. Sharma

  4. Department of Electronics and Communication Engineering, SEEC, Manipal University Jaipur, Jaipur, Rajasthan, India

    Sanyog Rawat

  5. Surface Characterization Group, NIMS, Nano Characterization Unit, Advanced Key Technologies Division, Tsukuba, Ibaraki, Japan

    Anirban Bandyopadhyay

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Das, G., Panda, S., Padhy, S.K. (2018). Quantum Particle Swarm Optimization Tuned Artificial Neural Network Equalizer. In: Pant, M., Ray, K., Sharma, T., Rawat, S., Bandyopadhyay, A. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 583. Springer, Singapore. https://doi.org/10.1007/978-981-10-5687-1_52

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  • DOI: https://doi.org/10.1007/978-981-10-5687-1_52

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-5686-4

  • Online ISBN: 978-981-10-5687-1

  • eBook Packages: EngineeringEngineering (R0)

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