Hierarchical Radial Basis Function Neural Networks for Classification Problems

  • Yuehui Chen
  • Lizhi Peng
  • Ajith Abraham
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)


The purpose of this study is to identify the hierarchical radial basis function neural networks and select important input features for each sub-RBF neural network automatically. Based on the pre-defined instruction/operator sets, a hierarchical RBF neural network can be created and evolved by using tree-structure based evolutionary algorithm. This framework allows input variables selection, over-layer connections for the various nodes involved. The HRBF structure is developed using an evolutionary algorithm and the parameters are optimized by particle swarm optimization algorithm. Empirical results on benchmark classification problems indicate that the proposed method is efficient.


Particle Swarm Optimization Particle Swarm Optimization Algorithm Radial Basis Function Neural Network Radial Basis Function Network Gaussian Radial Basis Function 
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

  • Yuehui Chen
    • 1
  • Lizhi Peng
    • 1
  • Ajith Abraham
    • 1
    • 2
  1. 1.School of Information Science and EngineeringJinan UniversityJinanP.R. China
  2. 2.IITA Professorship Program, School of Computer Science and Engg.Chung-Ang UniversitySeoulRepublic of Korea

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