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.

模糊空间的划分对于前提参数的识别非常重要，并且将高斯隶属函数应用于前提模糊集。然而，高斯隶属度函数的两个参数，中心和宽度，不容易确定。本文基于模糊c均值（FCM）和粒子群优化（PSO）算法，提出了一种优化高斯函数两个参数的TS模糊模型最优辨识方法。首先，我们使用FCM算法确定高斯中心进行粗调。然后，在高斯函数中心固定的情况下，使用PSO算法优化另一个可调参数，即高斯隶属函数的宽度，以实现微调，从而完成对模糊模型前提参数的识别。此外，递归最小二乘（RLS）算法用于识别结论参数。最后，通过仿真实例验证了该方法在TS模糊模型识别中的有效性，与其他识别方法相比，使用所描述的新颖识别方法可以获得更高的识别精度。