A fuzzy clustering algorithm enhancing local model interpretability |
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Authors: | J. L. Díez J. L. Navarro A. Sala |
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Affiliation: | (1) Departamento de Ingeniería de Sistemas y Automática, Universidad Politécnica de Valencia, Camino de Vera, 14, 46022 Valencia, Spain |
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Abstract: | In this work, simple modifications on the cost index of particular local-model fuzzy clustering algorithms are proposed in order to improve the readability of the resulting models. The final goal is simultaneously providing local linear models (reasonably close to the plant’s Jacobian) and clustering in the input space so that desirable characteristics (regarding final model accuracy, and convexity and smoothness of the cluster membership functions) are improved with respect to other proposals in literature. Some examples illustrate the proposed approach. |
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Keywords: | Fuzzy system identification Fuzzy clustering Interpretability Local models |
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