Abstract
Based on neural networks and fuzzy set theory, a hybrid Bayesian optimal classifier is proposed in the paper. It can implement fuzzy operation, and generate learning behaviour. The model firstly applies fuzzy membership function of the observed information to establish the posterior probabilities of original assumptions in Bayesian classification space, the classified results of all input information then are worked out. Across the calculation, the positive and reverse instances of all observed information are fully considered. The best classification result is acquired by incorporating with all possible classification results. The whole classifier adopts a hybrid four-layer forward neural network to implement. Fuzzy operations of input information are performed using fuzzy logic neurons. The investigation indicates that the proposed method expands the application scope and classification precision of Bayesian optimal classifier, and is an ideal patter classifier. In the end, an experiment in transformer insulation fault diagnosis shows the effectiveness of the method.
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References
Mitchhell Tom, M.: Machine Learning, 1st edn. McGraw-Hill Companies, Inc., Columbus (1997)
Opper, M., Haussler, D.: Generalization Behavior of Bayesian Optimal Prediction Algorithm for Learning a Perception. Physical Review Letters 66, 2677–2681 (1991)
Haussler, D., Kearns, M., Schapire, R.E.: Bounds on The Sample Complexity of Bayesian Learning, Using Information Theory and the VC Dimension. Machine Learning 14, 79–83 (1994)
Yang, L., Shang, Y., Zhou, Y.F., Yan, Z.: Probability Reasoning and Fuzzy Technique Applied for Identifying Power Transformer Fault. In: Proceedings of the CSEE, vol. 7, pp. 19–23 (2000)
Zhang, J.W., Zhao, D.G., Dong, L.W.: An Expert System for Transformer Fault Diagnosis Based on Fuzzy Mathematics. High Voltage Engineering 4, 6–8 (1998)
Chen, W.H., Liu, C.W., Tsai, M.S.: On-line Fault Diagnosis of Distribution Substations Using Hybrid Cause-Effect Network and Fuzzy Rule-based Method. IEEE Transactions on Power Delivery 15, 710–717 (2000)
Su, H.S., Li, Q.Z.: Transformer Insulation Fault Diagnosis Method Based on Rough Set and Fuzzy Set and Evidence Theory. In: The 6th World Congress on Intelligent Control and Automation, Dailan (2006)
Zadeh, L.: Fuzzy Set. Information and Control 8, 610–614 (1965)
Wang, Y.N.: Intelligent Information Processing, 1st edn. High Education Press, BJ (2003)
Zeng, H.: Intelligent Calculation, 1st edn. Chongqing University Press, Chongqing (2004)
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© 2006 Springer-Verlag Berlin Heidelberg
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Su, H., Li, Q., Dang, J. (2006). A Hybrid Bayesian Optimal Classifier Based on Neuro-fuzzy Logic. In: Jiao, L., Wang, L., Gao, Xb., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4221. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881070_49
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DOI: https://doi.org/10.1007/11881070_49
Publisher Name: Springer, Berlin, Heidelberg
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