A design of genetically oriented linguistic model with the aid of fuzzy granulation

In this paper, the fundamental idea of Linguistic Models (LM) introduced by Pedrycz is followed and their genetically oriented design framework is developed. The LM is designed by the use of fuzzy granulation realized via Context-based Fuzzy C-Means (CFCM) clustering. This clustering technique builds information granules in the form of fuzzy sets and develops clusters by preserving the homogeneity of the clustered patterns associated with the input and output space. However, it is difficult to optimize the number of linguistic context produced in the output space, the number of cluster generated by each context, and fuzzification factor related to fuzzy clustering. Thus, we perform simultaneous optimization of design parameters linking information granules in the input and output spaces based on Genetic Algorithm (GA). The experimental results on coagulant dosing process in a water purification plant reveal that the proposed method shows a good performance in comparison with Linear Regression (LR), Neural Networks (NN), Radial Basis Function Networks (RBFN), and LM itself.