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The classical fuzzy system modeling methods only consider the current scene where the training data are assumed fully collectable. However, if the available data from that scene are insufficient, the fuzzy systems trained will suffer from weak generalization for the modeling task in this scene. In order to overcome this problem, a fuzzy system with knowledge-leverage capability, which is known as a knowledge-leverage-based fuzzy system (KL-FS), is proposed in this paper. The KL-FS not only makes full use of the data from the current scene in the learning procedure but can effectively make leverage on the existing knowledge from the reference scene, e.g., the parameters of a fuzzy system obtained from a reference scene, as well. Specifically, a knowledge-leverage-based Mamdani-Larsen-type fuzzy system (KL-ML-FS) is proposed by using the reduced set density estimation technique integrating with the corresponding knowledge-leverage mechanism. The new fuzzy system modeling technique has been verified by experiments on synthetic and real-world datasets, where KL-ML-FS has better performance and adaptability than the traditional fuzzy modeling methods in scenarios with insufficient data.