Abstract
Topological structure of Probability Neural Network (PNN) is usually complex when it is trained with large-scale and high-redundant training samples. Aiming this problem, PNN is analyzed and simplified by using probability calculation and multiplication formula. At first, input data of training samples was statistical analyzed by using Principal Component Analysis (PCA). PNN topological structure was optimized based on the statistical results. Subsequently, a complete learning algorithm was provided to avoid the artificial set of smoothing parameters. And Simulated Annealing (SA) coefficient was introduced to increase learning speed and stability. Eventually, the optimized PNN was applied to real problem. The test result validated that the optimized PNN had simpler structure and higher efficiency than typical PNN in the application with large-scale and high-redundant training samples.
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© 2007 Springer Berlin Heidelberg
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Xing, J., Xiao, D., Yu, J. (2007). Principal Component Analysis Based Probability Neural Network Optimization. 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_127
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DOI: https://doi.org/10.1007/978-3-540-72393-6_127
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72392-9
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