Abstract
The division of fuzzy space is very important in the identification of premise parameters, and the Gaussian membership function is applied to the premise fuzzy set. However, the two parameters of Gaussian membership function, center and width, are not easy to be determined. In this paper, based on Fuzzy c-means (FCM) and particle swarm optimization (PSO) algorithm, a novel T-S fuzzy model optimal identification method of optimizing two parameters of Gaussian function is presented. Firstly, we use FCM algorithm to determine the Gaussian center for rough adjustment. Then, under the condition that the center of Gaussian function is fixed, the PSO algorithm is used to optimize another adjustable parameter, the width of the Gaussian membership function, to achieve fine-tuning, so as to complete the identification of prerequisite parameters of fuzzy model. In addition, the recursive least squares (RLS) algorithm is used to identify the conclusion parameters. Finally, the effectiveness of this method for T-S fuzzy model identification is verified by simulation examples, and the higher identification accuracy can be obtained by using the novel identification method described compared with other identification methods.
Highlights
In recent years, fuzzy model has been widely studied and has become an effective tool for complex system identification
In order to improve the identification accuracy of the model and prepare for further control, this article starts with changing the method of dividing the fuzzy space to improve the identification accuracy of the fuzzy model
The method adopted in this paper is to determine the center of Gaussian function by using the fuzzy c-means (FCM) algorithm, and to optimize its width by using particle swarm optimization (PSO) algorithm when the center has been determined and remains unchanged, so as to complete the fuzzy division of the premise parameters of the fuzzy model
Summary
Fuzzy model has been widely studied and has become an effective tool for complex system identification. How to combine the Gaussian membership function with the traditional FCM algorithm to improve the accuracy of model identification will be an interesting problem. FCRM method was used to optimize the center and width of Gaussian function to obtain higher modeling accuracy in [9]. The probability in the algorithm that indicates the spatial influence of the neighboring pixels on the center pixel plays a key role in this algorithm, and it obtains efficient method for calculating membership and updating prototypes by minimizing the new objective function of Gaussian based fuzzy c-means. The method adopted in this paper is to determine the center of Gaussian function by using the FCM algorithm, and to optimize its width by using PSO algorithm when the center has been determined and remains unchanged, so as to complete the fuzzy division of the premise parameters of the fuzzy model.
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