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Optimization of Non-fuzzy Neural Networks Based on Crisp Rules in Scatter Partition

  • Keon-Jun Park
  • Byun-Gon Kim
  • Kwan-Woong Kim
  • Jung-Won Choi
  • Yong-Kab Kim
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 301)

Abstract

We introduce a design of non-fuzzy neural networks that have crisp rules in scatter partition. To generate the crisp rules and construct the networks, we use hard c-means clustering algorithm. The partitioned local spaces indicate the crisp rules of the proposed networks. The consequence part of the rule is represented by polynomial functions. The coefficients of the polynomial functions are learned using back-propagation algorithm. In order to optimize the parameters of the proposed networks we use particle swarm optimization techniques. The proposed networks are evaluated with the example for nonlinear process.

Keywords

Non-fuzzy neural networks (NFNNs) Crisp rules Scatter partition Hard C-means clustering Particle swarm optimization 

Notes

Acknowledgments

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A1A2011835).

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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Keon-Jun Park
    • 1
  • Byun-Gon Kim
    • 2
  • Kwan-Woong Kim
    • 3
  • Jung-Won Choi
    • 1
  • Yong-Kab Kim
    • 1
  1. 1.Department of Information and Communication EngineeringWonkwang UniversityChonbukSouth Korea
  2. 2.Department of Electronic EngineeringKunsan National UniversityKunsanSouth Korea
  3. 3.Thunder Technology, Director in Digital Signer Processing TeamChonJuSouth Korea

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