Abstract
This paper proposes a new evolutionary algorithm (EA) which includes five different mutation operators: nodes merging, nodes deletion, penalizing, nodes inserting and hybrid training. The algorithm adaptively determines the structure and parameters of the radial basis function neural networks (RBFN). Many different radial basis functions with different sizes (covering area, locations and orientations) were used to construct the near-optimal RBFN during training. The resulting RBFN behaves even more powerful and requires fewer nodes than other algorithms. Simulation results in face recognition show that the system achieves excellent performance both in terms of error rates of classification and learning efficiency.
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Li, J., Huang, X., Li, R., Yang, S., Qi, Y. (2006). Evolutionary Algorithm of Radial Basis Function Neural Networks and Its Application in Face Recognition. In: Kłopotek, M.A., Wierzchoń, S.T., Trojanowski, K. (eds) Intelligent Information Processing and Web Mining. Advances in Soft Computing, vol 35. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-33521-8_7
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DOI: https://doi.org/10.1007/3-540-33521-8_7
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