Modified Regularity Criterion in Dynamic Fuzzy Modeling Applied to Industrial Processes

In practice, it is very difficult to achieve an accurate modeling for real-world systems. If the system structure is not precisely known and the number of potential inputs is large, the model has to be based primarily on data or heuristic information. The inherent characteristics of fuzzy logic theory makes it suitable for modeling this type of systems. In this paper a modification to the regularity criterion algorithm is proposed, aiming the reduction of computational time without loss of accuracy. The fuzzy models obtained using the modified regularity criterion algorithm are optimized by a real-coded genetic algorithm. Real data is used for the design and validation of two industrial processes used as examples. The proposed algorithm improves both the accuracy of the models and it reduces the computational time to obtain them