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- W2077882978 abstract "In this study, we propose a new method for building adaptive neuro-fuzzy inference systems (ANFIS) via datasets. In order to improve the performance of conventional ANFIS to handle noisy data, we focus on ameliorating the cluster-data space established from a given dataset. To achieve this, we propose a weighted clustering process in the joint input-output data space. Thus, during the clustering process, the cluster with the smallest potential distance, which is a combination of the Euclidean distance and the size of the clusters, has priority when obtaining the surveyed sample. Based on this principle, we formulate a new algorithm for synthesizing an ANFIS via the proposed data potential field, called ANFIS-PF, which has the following features: it establishes a data potential field that covers the entire initial data space, a cluster-data space is built based on the generated data potential field, and the ANFIS is synthesized using this cluster-data space. Finally, we performed experiments using datasets with and without noise to demonstrate the effectiveness of the proposed method in several applications, including dynamic-response noisy datasets obtained from a magnetorheological damper." @default.
- W2077882978 created "2016-06-24" @default.
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- W2077882978 date "2015-11-01" @default.
- W2077882978 modified "2023-10-18" @default.
- W2077882978 title "Design of a new adaptive neuro-fuzzy inference system based on a solution for clustering in a data potential field" @default.
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- W2077882978 doi "https://doi.org/10.1016/j.fss.2015.02.012" @default.
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