Abstract
The Fuzzy Min-Max (FMM) network is a supervised neural network classifier that forms hyperbox fuzzy sets for learning and classification. In this paper, we propose modifications to FMM in an attempt to improve its classification performance in situations when large hyperboxes are formed by the network. To achieve the goal, the Euclidean distance is computed after network training. We also propose to employ both the membership value of the hyperbox fuzzy sets and the Euclidean distance for classification. To assess the effectiveness of the modified FMM network, benchmark pattern classification problems are first used, and the results from different methods are compared. In addition, a fault classification problem with real sensor measurements collected from a power generation plant is used to evaluate the applicability of the modified FMM network. The results obtained are analyzed and explained, and implications of the modified FMM network in real environments are discussed.
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