A Framework of Modified Adaptive Fuzzy Inference Engine (MAFIE) and Its Application

This paper introduces a complete framework of Modified Adaptive Fuzzy Inference Engine (MAFIE) and its application. The fuzzy with hybridization schemes has become of research interest in versatile applications over the past decade. The fuzzy hybridizations models are quite popular among practitioners or researchers in various advanced promising fields to help solve problems with a small number of inputs. However, there are limitations faced by all popular fuzzy systems when they are applied to systems with a large number of inputs. A modified apriori algorithm technique is utilized to reduce a minimal set of decision rules based on input-output dataset. A TSK type fuzzy inference system is constructed by the automatic generation of membership functions and fuzzy rules by the hybrid fuzzy clustering (Fuzzy C-Means and Subtractive Clustering) and apriori algorithms techniques, respectively. The generated adaptive fuzzy inference engine is adjusted by the least-square estimator and a conjugate gradient descent algorithm towards better performance with a minimal set of fuzzy rules. The proposed MAFIE is able to reduce the number of fuzzy rules which increases exponentially when large input dimensions are involved. The performance of the proposed MAFIE is compared with other existing models when applied to pattern classification schemes using Fisher’s Iris and Wisconsin Breast Cancer benchmark datasets. The results are shown to be very competitive and MAFIE is ready for high dimension practical applications.