Role of Heuristic Methods with variable Lengths In ANFIS Networks Optimum Design and Training

ANFIS systems have been much considered due to their acceptable performance in terms of creation of fuzzy classifier and training. One main challenge in designing an ANFIS system is to achieve an efficient method with high accuracy and appropriate interpreting capability. Undoubtedly, type and location of membership functions and the way an ANFIS network is trained are of considerable effect on its performance. Up to present time, related researches have just found type and location of membership functions, and or suggested methods to train these networks. Main reason for lack of simultaneous determination of type and location of membership functions and training an ANFIS network is the length of standard versions of Heuristic methods being fixed. In this paper, a new version of optimization method of inclined planes will be introduced, primarily; while search factors could be variable. Then, achieved capability will be used for specifying type and location of membership functions and simultaneous training of a classifier based on adaptive neuro-fuzzy inference system (ANFIS). The proposed method on five benchmark datasets iris, Breast Cancer, Bupa Liver, Wine and Pima from the UCI database has been tested, which has different number of reference classes, different length of attribute vectors with appropriate complexity. Initially, the accuracy of the test dataset for each of the selected datasets was compared using the standard 10 folded cross validation method using the standardized version of the standard length.Then the same experiments were repeated by the proposed method and the results of applying the proposed method on the five aforementioned datasets were compared with the results of the heuristic methods with the standard length version. The comparative results show that the optimal and intelligent design of ANFIS classifier by variable length heuristics on five well-known datasets yields good and satisfactory results and in each of the five problems it has provided better answers than other design methods in the ANFIS classification system.