A Modified TSK Network and Its Application in Classification
نویسنده
چکیده
The paper presents a modified structure of Takaga-Sugeno-Kang (TSK) network with a fully automated building and learning algorithm. The modification has resulted in a great reduction of nonlinear parameters of the network (almost three times). The modified network can be initiated using Gustafson-Kessel clustering algorithm. After initiation all parameters are further fine-tuned by an gradient learning algorithm. With the proposed method of building and learning modified TSK network, users can easily generate automatically an effective TSK network for practical problems without needing deep knowledge of the fuzzy reasoning theory. As a numerical experiment, the solution has been tested in the problem of gas recognition as a fuzzy reasoning system with very high accuracy. Conclusions: The modified TSK network is a new fuzzy reasoning system which is more effective than the classical one. The simpler structure leads to shorter time of parameters adaptation.
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