Genetic Algorithms
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
IEEE Transactions on Evolutionary Computation
Similarity measures in fuzzy rule base simplification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Simplifying fuzzy rule-based models using orthogonal transformationmethods
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A systematic approach to a self-generating fuzzy rule-table forfunction approximation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy modeling with multivariate membership functions: gray-boxidentification and control design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Implementation of evolutionary fuzzy systems
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
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Interpretable decisions by means of similarities and modifiers
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Expert Systems with Applications: An International Journal
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In this paper, a new evolutionary algorithm for optimization of fuzzy models is proposed. For simultaneous optimization of structure and parameters of a fuzzy model, unique encoding scheme and appropriate evolutionary operators are proposed. There are three important aspects in fuzzy modeling: modeling accuracy, rule compactness, and interpretability of input membership functions. Thus, a new fitness function is proposed to consider the three objectives simultaneously. Through simulations on two well-known modeling problems, it is shown that the proposed algorithm is effective in finding an accurate fuzzy model with compact number of fuzzy rules. In addition, the fuzzy model uses well distributed membership functions that helps to increase interpretability of the fuzzy model.