A hybrid intelligent system and its application to fault detection and diagnosis

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Abstract

This paper proposes a hybrid system that integrates the SOM (Self-Organizing Map) neural network, the kMER (kernel-based Maximum Entropy learning Rule) algorithm and the Probabilistic Neural Network (PNN) for data visualization and classification. The rationales of this hybrid SOM-kMER-PNN model are explained, and the applicability of the proposed model is demonstrated using two benchmark data sets and a real-world application to fault detection and diagnosis. The outcomes show that the hybrid system is able to achieve comparable classification rates when compared to those from a number of existing classifiers and, at the same time, to produce meaningful visualization of the data sets.

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Teh, C. S., & Lim, C. P. (2006). A hybrid intelligent system and its application to fault detection and diagnosis. In Advances in Soft Computing (Vol. 36, pp. 165–175). https://doi.org/10.1007/978-3-540-36266-1_16

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