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Particle Swarm Optimization Based Higher Order Neural Network for Classification

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Computational Intelligence in Data Mining - Volume 1

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 31))

Abstract

The maturity in the use of both the feed forward neural network and Multilayer perception brought the limitations of neural network like linear threshold unit and multi-layering in various applications. Hence, a higher order network can be useful to perform nonlinear mapping using the single layer of input units for overcoming the drawbacks of the above-mentioned neural networks. In this paper, a higher order neural network called Pi-Sigma neural network with standard back propagation Gradient descent learning and Particle Swarm Optimization algorithms has been coupled to develop an efficient robust hybrid training algorithm with the local and global searching capabilities for classification task. To demonstrate the capacity of the proposed PSO-PSNN model, the performance has been tested with various benchmark datasets from UCI machine learning repository and compared with the resulting performance of PSNN, GA-PSNN. Comparison result shows that the proposed model obtains a promising performance for classification problems.

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Acknowledgments

This work is supported by Department of Science and Technology (DST), Ministry of Science and Technology, New Delhi, Govt. of India, under grants No. DST/INSPIRE Fellowship/2013/585.

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Authors and Affiliations

  1. Department of Computer Science Engineering and Information Technology, Veer Surendra Sai University of Technology, Burla, Sambalpur, 768018, Odisha, India

    Janmenjoy Nayak, Bighnaraj Naik & H. S. Behera

  2. Machine Intelligence Research Labs (MIR Labs), Washington, USA

    Ajith Abraham

  3. IT4 Innovations—Center of Excellence, VSB—Technical University of Ostrava, Ostrava, Czech Republic

    Ajith Abraham

Authors
  1. Janmenjoy Nayak
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  2. Bighnaraj Naik
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  3. H. S. Behera
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  4. Ajith Abraham
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Corresponding author

Correspondence to Janmenjoy Nayak .

Editor information

Editors and Affiliations

  1. University of Canberra, Canberra, Australia and University of South Australia, Adelaide, South Australia, Australia

    Lakhmi C. Jain

  2. Department of Computer Science and Engineering, Veer Surendra Sai University of Technology, Sambalpur, Odisha, India

    Himansu Sekhar Behera

  3. Computer Science & Engineering, Kalyani University, Nadia, West Bengal, India

    Jyotsna Kumar Mandal

  4. Dept. of Computer Science and Engineering, National Institute of Technology Rourkela, Rourkela, India

    Durga Prasad Mohapatra

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Nayak, J., Naik, B., Behera, H.S., Abraham, A. (2015). Particle Swarm Optimization Based Higher Order Neural Network for Classification. In: Jain, L., Behera, H., Mandal, J., Mohapatra, D. (eds) Computational Intelligence in Data Mining - Volume 1. Smart Innovation, Systems and Technologies, vol 31. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2205-7_38

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  • DOI: https://doi.org/10.1007/978-81-322-2205-7_38

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2204-0

  • Online ISBN: 978-81-322-2205-7

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