Abstract: It is a proven fact now that fuzzy logic is a powerful problem-solving methodology with wide range of applications in industrial control, consumer electronics, management, medicine, expert systems and information technology. It provides a simple way to draw definite conclusions from vague, ambiguous or imprecise and incomplete information. It is a natural way of making a decision and is very close to the way the human beings think and make decisions even under highly uncertain environments. Fuzzy Classifiers are powerful class of fuzzy systems. Evolving fuzzy classifiers from numerical data has assumed lot of remarks in the recent past. This paper proposes a method of evolving fuzzy classifiers using a three-step technique. In the first step, a modified Fuzzy C–Means Clustering technique is applied to generate membership functions. In the next step, rule base is generated using cuckoo search algorithm. The third step was used to reduce the size of the generated rule base. By this method rule explosion issue was successfully tackled. The proposed method was applied using MATLAB. The approach was tested on a very well-known multi-dimensional classification data sets i.e. Iris Data. The performance of the proposed method was very encouraging. Further the algorithm is implemented on a Mamdani type control model for a battery charger data set. This integrated approach was able to evolve model quickly.

Keywords: Fuzzy Rules, Mamdani Control Model, Cuckoo Algorithm.

| DOI: 10.17148/IARJSET.2020.7615