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Multiresolution Neural Networks Based on Immune Particle Swarm Algorithm

  • Li Ying
  • Deng Zhidong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4221)

Abstract

Inspired by the theory of multiresolution analysis (MRA) of wavelets and artificial neural networks, a multiresolution neural network (MRNN) for approximating arbitrary nonlinear functions is proposed in this paper. MRNN consists of a scaling function neural network (SNN) and a set of sub-wavelet neural networks, in which each sub-neural network can capture the specific approximation behavior (global and local) at different resolution of the approximated function. The structure of MRNN has explicit physical meaning, which indeed embodies the spirit of multiresolution analysis of wavelets. A hierarchical construction algorithm is designed to gradually approximate unknown complex nonlinear relationship between input data and output data from coarse resolution to fine resolution. Furthermore, A new algorithm based on immune particle swarm optimization (IPSO) is proposed to train MRNN. To illustrate the effectiveness of our proposed MRNN, experiments are carried out with different kinds of wavelets from orthonormal wavelets to prewavelets. Simulation results show that MRNN provides accurate approximation and good generalization.

Keywords

Particle Swarm Multiresolution Analysis Wavelet Neural Network Translation Parameter Orthonormal Wavelet 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Li Ying
    • 1
    • 2
  • Deng Zhidong
    • 1
  1. 1.Department of Computer Science and Technology State Key Laboratory of Intelligent Technology and SystemsTsinghua UniversityBeijing
  2. 2.College of Computer Science and Technology Jilin UniversityChangchunP.R. China

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