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A Resource Limited Immune Approach for Evolving Architecture and Weights of Multilayer Neural Network

  • Xiaoyang Fu
  • Shuqing Zhang
  • Zhenping Pang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6145)

Abstract

A resource limited immune approach (RLIA) was developed to evolve architecture and initial connection weights of multilayer neural networks. Then, with Back-Propagation (BP) algorithm, the appropriate connection weights can be found. The RLIA-BP classifier, which is derived from hybrid algorithm mentioned above, is demonstrated on SPOT multi-spectral image data, vowel data and Iris data effectively. The simulation results demonstrate that RLIA-BP classifier possesses better performance comparing with Bayes maximum-likelihood classifier, k-nearest neighbor classifier (k-NN), BP neural network (BP-MLP) classifier and Resource limited artificial immune classifier (AIRS) in pattern classification.

Keywords

resource limited immune approach (RLIA) evolutionary artificial neural network (EANN) pattern classification 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Xiaoyang Fu
    • 1
  • Shuqing Zhang
    • 2
  • Zhenping Pang
    • 1
  1. 1.College of Computer Science and TechnologyJilin UniversityZhuhaiChina
  2. 2.Northeast Institute of Geography and AgroecologyChinese Academy of SciencesChangchunChina

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