Evolutionarily adjusting membership functions in Takagi-Sugeno fuzzy systems

Authors:
Tzung-/Pei Hong;Wei-/Tee Lin;Chun-/Hao Chen;Chen-/Sen Ouyang
Affiliations:
Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, No. 700, Kaohsiung University Rd., Nanzih District, 811 Kaohsiung, Taiwan/ Department of Co ...;Department of Computer Science and Information Engineering, National Cheng-/Kung University, No. 1, University Road, Tainan City 701, Taiwan.;Department of Computer Science and Information Engineering, Tamkang University Taipei, No. 151, Ying-/Chuan Road, Tamsui, Taipei County 25137, Taiwan.;Department of Computer Science and Information Engineering, I-/Shou University, Kaohsiung, No. 1, Sec. 1, Syuecheng Rd., Dashu Township, Kaohsiung County 840,Taiwan
Venue:
International Journal of Intelligent Information and Database Systems
Year:
2011

Citing 8
Cited 2

Evolving fuzzy rule based controllers using genetic algorithms

Fuzzy Sets and Systems
Fuzzy connectives based crossover operators to model genetic algorithms population diversity

Fuzzy Sets and Systems
A learning process for fuzzy control rules using genetic algorithms

Fuzzy Sets and Systems
A genetic algorithm for optimizing Takagi-Sugeno fuzzy rule bases

Fuzzy Sets and Systems
A New Approach to Fuzzy Classifier Systems

Proceedings of the 5th International Conference on Genetic Algorithms
A GA-based Fuzzy Mining Approach to Achieve a Trade-off Between Number of Rules and Suitability of Membership Functions

Soft Computing - A Fusion of Foundations, Methodologies and Applications
Local identification of prototypes for genetic learning of accurate TSK fuzzy rule-based systems: Research Articles

International Journal of Intelligent Systems
SLAVE: a genetic learning system based on an iterative approach

IEEE Transactions on Fuzzy Systems

Efficient differential evolution algorithm-based optimisation of fuzzy prediction model for time series forecasting

International Journal of Intelligent Information and Database Systems
Efficient differential evolution algorithm-based optimisation of fuzzy prediction model for time series forecasting

International Journal of Intelligent Information and Database Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Fuzzy set theory has been used more and more frequently in intelligent systems because of its simplicity and similarity to human reasoning. It usually uses a fuzzy inference system to handle new cases for making decisions or controlling actions. In the past, Takagi and Sugeno proposed a well-known fuzzy model, namely TS fuzzy model, to improve the precision of inference results. In this paper, we try to automatically adjust the membership functions appropriate for the TS fuzzy model. A GA-based learning algorithm is thus proposed to achieve the purpose. The proposed approach considers the shapes of membership functions in fitness evaluation in addition to the accuracy. The experimental results show that the proposed approach can derive the membership functions in the Takagi-Sugeno system with low errors and good shapes.