Mixture Data-driven Takagi-Sugeno Fuzzy Model
The conventional Takagi-Sugeno(T-S)fuzzy model is an effective tool used to approximating behaviors of nonlinear systems on the basis of precise and certain input and output observations.In some situations,however,we can only obtain mixture of precise data(for input variables),imprecise and uncertain data(for output variable/response).This paper presents a method used to constructing T-S fuzzy model in such case where the imprecise and uncertain output observations are represented as fuzzy belief function,and then proposes the socalled mixture data-driven T-S fuzzy model,among which,the consequents are identified by using a novel fuzzy evidential Expectation-Maximization(EM)algorithm and the antecedents are automatically constructed by using a data-driven strategy,considering both the accuracy and complexity of model.The performance of such mixture data data-driven fuzzy model was validated by conducting some unreliable sensor experiments.The numerical simulations suggest that the proposed fuzzy model can be used to approximate nonlinear systems with high accuracy when the outputs of systems are imprecisely and uncertainly observed.
T-S fuzzy model imprecise and uncertaint data data-driven belief function EM algorithm
Zhi-gang Su Babak Rezaee Pei-hong Wang
Dept. of Energy Information and Automation,School of Energy and Environment,Southeast University,Nan Dept. of industrial engineering,Amirkabir university of technology,P.O. Box 15875-4413,Tehran,Iran
国际会议
厦门
英文
24-30
2014-08-19（万方平台首次上网日期，不代表论文的发表时间）