Stochastic global optimization methods. part 1: clustering methods
Mathematical Programming: Series A and B
Stochastic global optimization methods. part 11: multi level methods
Mathematical Programming: Series A and B
Cooling schedules for optimal annealing
Mathematics of Operations Research
Simulated annealing—to cool or not
Systems & Control Letters
Automatically determine the membership function based on the maximum entropy principle
Information Sciences: an International Journal
Fuzzy logic: intelligence, control, and information
Fuzzy logic: intelligence, control, and information
Neuro-fuzzy systems for function approximation
Fuzzy Sets and Systems - Special issue on analytical and structural considerations in fuzzy modeling
A new algorithm for floorplan design
DAC '86 Proceedings of the 23rd ACM/IEEE Design Automation Conference
A new approach of neuro-fuzzy learning algorithm for tuning fuzzy rules
Fuzzy Sets and Systems
Data mining with a simulated annealing based fuzzy classification system
Pattern Recognition
International Journal of Approximate Reasoning
Development of a systematic methodology of fuzzy logic modeling
IEEE Transactions on Fuzzy Systems
Application of statistical information criteria for optimal fuzzy model construction
IEEE Transactions on Fuzzy Systems
Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement
IEEE Transactions on Fuzzy Systems
GA-fuzzy modeling and classification: complexity and performance
IEEE Transactions on Fuzzy Systems
Designing fuzzy inference systems from data: An interpretability-oriented review
IEEE Transactions on Fuzzy Systems
Compact and transparent fuzzy models and classifiers through iterative complexity reduction
IEEE Transactions on Fuzzy Systems
Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base
IEEE Transactions on Fuzzy Systems
Generating an interpretable family of fuzzy partitions from data
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Determining the number of postal units in the network - Fuzzy approach, Serbia case study
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
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This paper presents the use of simulated annealing metaheuristic for tuning Mamdani type fuzzy models. Structure of the Mamdani fuzzy model is learned from input-output data pairs using Wang and Mendel's method and fuzzy c-means clustering algorithm. Then, parameters of the fuzzy system are tuned through simulated annealing. In this paper, we perform experiments to examine effects of (a) initial solution generated by Wang and Mendel's method and fuzzy c-means clustering method, (b) membership function update procedure, (c) probability parameter for the calculation of the initial temperature, (d) temperature update coefficient used for cooling schedule, and (e) randomness level in the disturbance mechanism used in simulated annealing algorithm on the tuning of Mamdani type fuzzy models. Experiments are performed with Mackey-Glass chaotic time series. The results indicate that Wang and Mendel's method provides better starting configuration for simulated annealing compared to fuzzy c-means clustering method, and for the membership function update parameter, MFChangeRate@? (0,1], and the probability parameter for the calculation of the initial temperature, P"0@? (0,1), values close to zero produced better results.