Application of evolving Takagi-Sugeno fuzzy model to nonlinear system identification
Applied Soft Computing
Fuzzy classifier identification using decision tree and multiobjective evolutionary algorithms
International Journal of Approximate Reasoning
Developing a bioaerosol detector using hybrid genetic fuzzy systems
Engineering Applications of Artificial Intelligence
A hybrid coevolutionary algorithm for designing fuzzy classifiers
Information Sciences: an International Journal
Data-driven fuzzy modeling for Takagi-Sugeno-Kang fuzzy system
Information Sciences: an International Journal
A dynamically constrained multiobjective genetic fuzzy system for regression problems
IEEE Transactions on Fuzzy Systems
Automatic modeling of fuzzy systems using particle swarm optimization
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
Expert Systems with Applications: An International Journal
Automatic adaptive modeling of fuzzy systems using particle swarm optimization
Transactions on computational science VIII
Automatic adaptive modeling of fuzzy systems using particle swarm optimization
Transactions on computational science VIII
Identification of transparent, compact, accurate and reliable linguistic fuzzy models
Information Sciences: an International Journal
A global-local optimization approach to parameter estimation of RBF-type models
Information Sciences: an International Journal
Feedback controlled particle swarm optimization and its application in time-series prediction
Expert Systems with Applications: An International Journal
Genetic fuzzy system for data-driven soft sensors design
Applied Soft Computing
Linguistic fuzzy model identification based on PSO with different length of particles
Applied Soft Computing
Adaptability, interpretability and rule weights in fuzzy rule-based systems
Information Sciences: an International Journal
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Developing Takagi-Sugeno fuzzy models by evolutionary algorithms mainly requires three factors: an encoding scheme, an evaluation method, and appropriate evolutionary operations. At the same time, these three factors should be designed so that they can consider three important aspects of fuzzy modeling: modeling accuracy, compactness, and interpretability. This paper proposes a new evolutionary algorithm that fulfills such requirements and solves fuzzy modeling problems. Two major ideas proposed in this paper lie in a new encoding scheme and a new fitness function, respectively. The proposed encoding scheme consists of three chromosomes, one of which uses unique chained possibilistic representation of rule structure. The proposed encoding scheme can achieve simultaneous optimization of parameters of antecedent membership functions and rule structures with the new fitness function developed in this paper. The proposed fitness function consists of five functions that consider three evaluation criteria in fuzzy modeling problems. The proposed fitness function guides evolutionary search direction so that the proposed algorithm can find more accurate compact fuzzy models with interpretable antecedent membership functions. Several evolutionary operators that are appropriate for the proposed encoding scheme are carefully designed. Simulation results on three modeling problems show that the proposed encoding scheme and the proposed fitness functions are effective in finding accurate, compact, and interpretable Takagi-Sugeno fuzzy models. From the simulation results, it is shown that the proposed algorithm can successfully find fuzzy models that approximate the given unknown function accurately with a compact number of fuzzy rules and membership functions. At the same time, the fuzzy models use interpretable antecedent membership functions, which are helpful in understanding the underlying behavior of the obtained fuzzy models