Data-based system modeling using a type-2 fuzzy neural network with a hybrid learning algorithm.
We propose a novel approach for building a type-2 neural-fuzzy system from a given set of input-output training data. A self-constructing fuzzy clustering method is used to partition the training dataset into clusters through input-similarity and output-similarity tests. The membership function associated with each cluster is defined with the mean and deviation of the data points included in the cluster. Then a type-2 fuzzy Takagi-Sugeno-Kang IF-THEN rule is derived from each cluster to form a fuzzy rule base. A fuzzy neural network is constructed accordingly and the associated parameters are refined by a hybrid learning algorithm which incorporates particle swarm optimization and a least squares estimation. For a new input, a corresponding crisp output of the system is obtained by combining the inferred results of all the rules into a type-2 fuzzy set, which is then defuzzified by applying a refined type reduction algorithm. Experimental results are presented to demonstrate the effectiveness of our proposed approach.
Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan. yuan@water.ee.nsysu.edu.tw
Chi-Yuan
CY
Yeh
Wen-Hau Roger
WH
Jeng
Shie-Jue
SJ
Lee
D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D005246:Feedback; D017143:Fuzzy Logic; D008962:Models, Theoretical; D016571:Neural Networks, Computer