AN IMPROVED FUZZY C-MEANS CLUSTERING ALGORITHM FOR RBF NETWORKS
Journal Title: Advance Research Journal of Multidisciplinary Discoveries - Year 2018, Vol 31, Issue 31
Abstract
In fuzzy C-means (FCM) clustering, each data point belongs to a cluster to a degree specified by a membership grade. FCM partitions a collection of vectors in c fuzzy groups and finds a cluster center in each group such that the dissimilarity measure is minimized. This paper presented a training algorithm for the radial basis function (RBF) network using improved Fuzzy C-means (IFCM) clustering method which is the modified version of FCM clustering method based on weight readjustment for each attributed.The training algorithm which uses IFCM clustering method to train the network gain better accuracy in predictions and reduced network architecture compared to the standard RBF networks. The proposed training algorithm was implemented with RBF networks in MATLAB, therefore the new network will undergo a hybrid learning process. The networks called improved Fuzzy C-means Clustering–Radial Basis Function Network (IFCM/RBF) was tested against the standard RBF network and the networks called standard Fuzzy C-means Clustering-RBF network (FCM/RBF) in predictions. The experimental models were tested on three real world application problems, particularly in Air pollutant problem, Biochemical Oxygen Demand (BOD) problem, and Phytoplankton problem, which yield promising results.
Authors and Affiliations
Dr. Lim Eng Aik, Tan Wei Hong, Ahmad Kadri Junoh
Keywords
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- EP ID EP424995
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