Title :
An Internal/Interconnection Recurrent Type-2 Fuzzy Neural Network (IRT2FNN) for dynamic system identification
Author :
Yang-Yin Lin ; Chang, Jyh-Yeong ; Lin, Yang-Yin
Author_Institution :
Dept. of Electr. & Control Eng., Nat. Chiao-Tung Univ., Hsinchu, Taiwan
Abstract :
This paper proposes an Internal /Interconnection Recurrent Type-2 Fuzzy Neural Network (IRT2FNN) for dynamic system identification. The antecedent part of IRT2FNN forms a self and interconnection feedback loop by feeding the past and current firing strength of each rule. The TSK-type consequent part is a linear model of exogenous inputs with interval weights. The initial rule base in the IRT2FNN is empty, and an on-line constructing method is proposed to generate fuzzy rules which flexibly partition the input space. The recurrent structure in the IRT2FNN enable to handle dynamic system identification problems with a priori knowledge of system input and output delay numbers. Simulations on dynamic system identification verify the performance of IRT2FNN with clean and noisy outputs as well.
Keywords :
fuzzy neural nets; fuzzy set theory; recurrent neural nets; TSK-type consequent part; dynamic system identification; fuzzy rules; interconnection feedback loop; interconnection recurrent neural network; internal recurrent neural network; online construction method; type-2 fuzzy neural network; Computational modeling; Noise measurement; Recurrent neural network; dynamic system identification; on-line fuzzy clustering; recurrent fuzzy neural networks; type-2 fuzzy systems;
Conference_Titel :
Systems Man and Cybernetics (SMC), 2010 IEEE International Conference on
Conference_Location :
Istanbul
Print_ISBN :
978-1-4244-6586-6
DOI :
10.1109/ICSMC.2010.5641840