Evolutionary fuzzy system models with improved fuzzy functions and its application to industrial process

This paper presents a new evolutionary fuzzy system modeling strategy alternative to fuzzy rule bases, and does not entail if...then rule base structure. The new approach, which is based on improved fuzzy functions with genetic algorithms, is proposed to reduce complexity of earlier fuzzy system models and improve modeling accuracy. Structure identification of the new approach is based on a supervised improved fuzzy clustering (IFC) method with a dual optimization algorithm, which yields improved membership values. The merit of the proposed FSM is that uncertain information on natural grouping of data samples, i.e., membership values, is utilized as additional predictors while structuring fuzzy functions. Presented model is applied to desulphurization process of a steel company in Canada. It is shown that proposed approach is superior in comparison to earlier fuzzy, neuro-fuzzy, and non-fuzzy system models in terms of robustness and error reduction.