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This paper presents a similarity-based fuzzy learning approach with a two-layer optimization scheme to make fuzzy systems more compact and accuracy. Two ways to improve fuzzy learning algorithms are considered in this paper, including the pruning strategy for simplifying the structure of fuzzy systems and the optimization scheme for parameters optimization. So far as the pruning strategy is concerned,...
This article presents a clustering-based approach to fuzzy system identification. In order to construct an effective initial fuzzy model, this article tries to present a modular method to identify fuzzy systems based on a hybrid clustering-based technique. Moreover, the determination of the proper number of clusters and the appropriate location of clusters are one of primary considerations on constructing...
This paper proposes an evolving-construction scheme for fuzzy systems (ECSFS). ECSFS begins with a simple fuzzy system and evolves its structure by adding more fuzzy terms and rules to achieve a better accuracy in a "greedy'' way. An interesting feature of ECSFS is that it is able to automatically locate mathematically meaningful points, such as the extremum and inflexion points of the approximated...
Clustering algorithms are often used for fuzzy system identification. However, most clustering algorithms do not consider the outputs for clustering. In addition, these algorithms do not consider how to obtain the optimal number of clusters. Without the optimal number of clusters, the final set of clusters may be inappropriate. To address this, this paper presents an Input-Output Clustering (IOC)...
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