Abstract
Genetic algorithm is introduced to network optimization to overcome the limitation of conventional SOM network. Based on this idea, a new model of structural adapting self-organizing neural network is proposed. In this model, each neuron is regarded as individual of evolutionary population and three operators are constructed as follows:growing operator, pruning operator and stochastic creating operator. In the algorithm, the accumulative error of neuron is selected as fitness function each iteration, and the neurons on compete layer are generated or deleted adaptively according to the values of fitness function until there is not any change of neuron on compete layer. Simulation experiments indicate that this structural adaptive network has better performance than conventional SOM network.
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References
Kohonen, T.: Self-Organization and Associative Memory, 3rd edn. Springer, Berlin (1989)
Kohonen, T.: Self-Organizing Maps. Springer, Berlin (1995)
Villmann, T., Der, R., Hermann, M., Martinetz, M.: Topology Preservation in Self-Organizing Feature Maps: Exact Definition and Measurement. IEEE Trans. Neural Networks 8, 256–266 (1997)
Fritzke, B.: Growing Cell Structures-a Self-Organizing Network for Unsupervised and Supervised Learning. Neural Network 7, 1441–1460 (1994)
Wu, Z., Yan, P.F.: A Study on Structural Adapting Self-Organizing Neural Network. Acta Electronic Sinica 27, 55–58 (1999)
Alahakoon, D., Halgamuge, S.K.: Dynamic Self-Organizing Maps with Controlled Growth for Knowledge Discovery. IEEE Trans. Neural Networks 12, 153–158 (2000)
Goldberg, D.E.: Genetic Algorithm in Search Optimization and Machine Learning. Addison-Wesley Publishing, Reading (1989)
Ritter, H.J., Kohonen, T.: Self-Organizing Semantic Maps. Biol. Cybern. 61, 241–254 (1989)
Chun, M., Chang, H.T.: Fast Self-Organizing Feature Map Algorithm. IEEE Trans. Neural Networks 11, 721–733 (2000)
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Xu, X., Zeng, W., Zhao, Z. (2007). A Structural Adapting Self-organizing Maps Neural Network. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72393-6_109
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DOI: https://doi.org/10.1007/978-3-540-72393-6_109
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72392-9
Online ISBN: 978-3-540-72393-6
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