Abstract
The fuzzy self-organizing map neural network using kernel principal component analysis is presented and a hybrid-learning algorithm (KPCA-FSOM) divided into two stages is proposed to train this network. The first stage, the KPCA algorithm is applied to extract the features of nonlinear data. The second stage, combining both the fuzzy theory and locally-weight distortion index to extend SOM basic algorithm, the fuzzy SOM algorithm is presented to train the SOM network with features gained. A real life application of KPCA-FSOM algorithm in classifying data of acrylonitrile reactor is provided. The experimental results show this algorithm can obtain better clustering and network after training can more effectively monitor yields.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kohonen, T.: Self-Organizing Maps. Springer, New York (1987)
Kohonen, T.: The Self-Organizing Map. Neurocomputing 21, 1–6 (1998)
Jin, H., Shum, W.H., Leung, K.S.: Expanding Self-Organizing Map for data visulization and Cluster analysis. Information Sciences 163, 157–173 (2004)
Guha, S., Rastogi, R., Shim, K.: An efficient clustering for large database. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 73–84 (1998)
Curry, B., Morgan, P.H.: Evaluating Kohonen’s learning rule: An approach through genetic algorithms. European Journal of Operational Research 154, 191–205 (2004)
Vesanto, J., Alhoniemi, E.: Clustering of the self-organizing map. IEEE Transactions on Neural Networks 11, 586–600 (2000)
Wu, S., Chow, T.W.S.: Clustering of the self-organizing map using a clustering validity index based on inter-cluster and intra-cluster density. Pattern Recognition 37, 175–188 (2004)
Su, M.C., Chang, H.T.: A new model of self-organizing neural networks and its application in data projection. IEEE Transaction on Neural Network 12, 153–158 (2001)
Cristianini, N., Taylor, J.S.: An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge Press (2003)
Jade, A.M., Srikanth, B., Jayaraman, V.K.: Feature extraction and denoising using kernel PCA. Chemical Engineering Science 58, 4441–4448 (2003)
Chen, S., Zhu, Y.L.: Subpattern-based principal component analysis. Pattern Recognition 37, 1081–1083 (2004)
Kuo, R.T., Chi, S.C., Teng, P.W.: Generalized part family formation through fuzzy self-organizing feature map neural network. Computers & Industrial Engineering 40, 79–100 (2001)
Li, S.T., Shue, L.Y.: Data mining to aid policy making in air pollution management. Expert Systems with Applications 27, 331–340 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lv, Q., Yu, Js. (2005). Fuzzy Self-Organizing Map Neural Network Using Kernel PCA and the Application. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539087_10
Download citation
DOI: https://doi.org/10.1007/11539087_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28323-2
Online ISBN: 978-3-540-31853-8
eBook Packages: Computer ScienceComputer Science (R0)