Improving recognition and generalization capability of back-propagation NN using a self-organized network inspired by immune algorithm (SONIA)

Muhammad Rahmat Widyanto, Hajime Nobuhara, Kazuhiko Kawamoto, Kaoru Hirota, Benyamin Kusumo Putro

Research output: Contribution to journalArticlepeer-review

50 Citations (Scopus)

Abstract

To improve recognition and generalization capability of back-propagation neural networks (BP-NN), a hidden layer self-organization inspired by immune algorithm called SONIA, is proposed. B cell construction mechanism of immune algorithm inspires a creation of hidden units having local data recognition ability that improves recognition capability. B cell mutation mechanism inspires a creation of hidden units having diverse data representation characteristics that improves generalization capability. Experiments on a sinusoidal benchmark problem show that the approximation error of the proposed network is 1/17 times lower than that of BP-NN. Experiments on real time-temperature-based food quality prediction data shows that the recognition capability is 18% improved comparing to that of BP-NN. The development of the world first time-temperature-based food quality prediction demonstrates the real applicability of the proposed method in the field of food industry.

Original languageEnglish
Pages (from-to)72-84
Number of pages13
JournalApplied Soft Computing Journal
Volume6
Issue number1
DOIs
Publication statusPublished - 1 Nov 2005

Keywords

  • Back-propagation
  • Food quality prediction
  • Immune algorithm
  • Self-organization

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