Fuzzy Membership Function Generation: A Modified Approach with Improved Performance

This paper presents a new approach to generate fuzzy membership functions. We propose to use learning vector quantization (LVQ) before using FCM to initialize it with number and initial location of cluster centers. We also propose to use modified fuzzy C-means algorithm (MCM) to provide modified shapes of membership functions at extremes. The simulation has shown that the proposed algorithm has better performance as it provide the optimum solution, speeds up the rate of convergence, does not require to specify the number of clusters beforehand, does not require to specify the partition matrix randomly and generates membership functions that provide a smooth variation of the control action when the parameters are at extreme. Keywords: Fuzzy C-Means (FCM), Learning vector Quantization (LVQ), Modified Fuzzy C-Means (MCM).