期刊名称：International Journal of Computer Information Systems and Industrial Management Applications
印刷版ISSN：2150-7988
电子版ISSN：2150-7988
出版年度：2013
卷号：5
页码：662-670
出版社：Machine Intelligence Research Labs (MIR Labs)
摘要：This paper introduces a complete framework of Modified Adaptive Fuzzy Inference E ngine (MAFIE) and its application. The fuzzy with hybridization schemes has become of research interest in versatile applications over the past decade. T he 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 wit h 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 aut omatic 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 f uzzy 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. T he results are shown to be very competitive and MAFIE is ready for high dimension practical applications.
