Abstract
This paper describes a novel adaptive network, which agglomerates a procedure based on the fuzzy min-max clustering method, a supervised ART (Adaptive Resonance Theory) neural network, and a constructive conflict-resolving algorithm, for pattern classification. The proposed classifier is a fusion of the ordering algorithm, Fuzzy ARTMAP (FAM) and the Dynamic Decay Adjustment (DDA) algorithm. The network, called Ordered FAMDDA, inherits the benefits of the trio, viz. an ability to identify a fixed order of training pattern presentation for good generalisation; stable and incrementally learning architecture; and dynamic width adjustment of the weights of hidden nodes of conflicting classes. Classification performance of the Ordered FAMDDA is assessed using two benchmark datasets. The performances are analysed and compared with those from FAM and Ordered FAM. The results indicate that the Ordered FAMDDA classifier performs at least as good as the mentioned networks. The proposed Ordered FAMDDA network is then applied to a condition monitoring problem in a power generation station. The process under scrutiny is the Circulating Water (CW) system, with prime attention to condition monitoring of the heat transfer efficiency of the condensers. The results and their implications are analysed and discussed.
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
Berthold, M.R. and Diamond, J. (1995), “Boosting the performance of rbf networks with dynamic decay adjustment” in Tesauro, G., Touretzky, D.S., and Leen, T.K., editors, Advances in Neural Information Processing Systems, vol. 7, Cambridge, MA, MIT Press.
Blake, C. and Merz, C. (1998), UCI Repository of Machine Learning Databases, URL http: //www.ics.uci.edu/~mlearn/MLRepository.html
Carpenter, G. and Grossberg, S. (1987), “A massively parallel architecture for a self-organizing neural pattern recognition machine” Computer Vision, Graphics and Image Processing, vol. 37, pp. 54–115.
Carpenter, G., Grossberg, S., and Rosen, D. (1991), “Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system” Neural Networks, vol. 4, pp. 759–771.
Carpenter, G., Grossberg, S., Markuzon, N., Reynolds, J., and Rosen, D., (1992), “Fuzzy ARTMAP: A neural network architecture for incremental learning of analog multidimensional maps” IEEE Trans. Neural Networks, vol. 3, pp. 698–713.
Dagher, I., Georgiopoulos, M., Heileman, G.L., and Bebis, G. (1999), “An ordering algorithm for pattern presentation in fuzzy ARTMAP that tends to improve generalization performance” IEEE Trans. Neural Networks, vol. 10, pp. 768–778.
Efron, B. (1979), “Bootstrap methods: another look at the Jackknife” The Annals of Statistics, vol. 7, pp. 1–26.
Grossberg, S. (1976), “Adaptive pattern recognition and universal recoding ii: feedback, expectation, olfaction, and illusions” Biological Cybernetics, vol. 23, pp. 187–202.
Huber, K.-P. and Berthold, M.R. (1995), “Building precise classifiers with automatic rule extraction” Proceedings of the IEEE Int. Conf. Neural Networks, vol. 3, pp. 1263–1268.
Lim, C.P. and Harrison, R.F. (1997), “An incremental adaptive network for on-line supervised learning and probability estimation” Neural Networks, vol. 10, pp. 925–939.
Mangasarian, O.L. and Wolberg, W.H. (1990), “Cancer diagnosis via linear programming” SIAM News, vol. 23, pp. 1–18.
Moody, M.J. and Darken, C.J. (1989), “Fast learning in networks of locally-tuned processing units” Neural Computation, vol. 1, pp. 281–294.
Murphy, P. and Ana, D. (1994), UCI Repository of Machine Learning Databases, Dept. Comput. Sci. Univ. California, Irvine, CA, Tech. Rep.. Available http://www.ics.uci.edu/~mlearn/MLRepository.html
Riedmiller, M. and Braun, H. (1993), “A direct adaptive method for faster backprogation learning: the RPROP algorithm” Proceedings of the IEEE Int. Conf. Neural Networks, vol. 1, pp. 586–591.
Simpson, P.K. (1992), “Fuzzy min-max neural networks–-Part 1: Classification” IEEE Trans. Neural Networks, vol. 3, pp. 776–786.
System Description and Operating Procedures (1999), Prai Power Station Stage 3, vol. 14.
Tou, J.T. and Gonzalez, R.C. (1976), Pattern Recognition Principles, Reading, MA: Addison-Wesley.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer
About this paper
Cite this paper
Chiang Tan, S., Rao, M., Lim, C.P. (2006). An Adaptive Fuzzy Min-Max Conflict-Resolving Classifier. In: Abraham, A., de Baets, B., Köppen, M., Nickolay, B. (eds) Applied Soft Computing Technologies: The Challenge of Complexity. Advances in Soft Computing, vol 34. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31662-0_5
Download citation
DOI: https://doi.org/10.1007/3-540-31662-0_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31649-7
Online ISBN: 978-3-540-31662-6
eBook Packages: EngineeringEngineering (R0)