一個針對T-S模糊模型之新式建模方法

A Novel Modeling Method for T-S Fuzzy Model

蘇宏恩(Hong-En Su)

指導教授 ： 蔡舜宏 ； 吳明川

國立臺北科技大學/機電學院/自動化科技研究所/碩士(2013年)

摘要

本文提出一個針對T-S模糊模型的新式建模方法，首先利用模糊c-平均值(Fuzzy C-Means, FCM)演算法將資料點做分群並找出合適的分群數，再將此數目當作規則數，把未知系統分成數個線性系統。接著結合粒子群最佳化演算法(Particle Swarm Optimization, PSO)與模糊c-回歸模型(Fuzzy C-Regression Model, FCRM)演算法來找出資料點與這些線性系統的模糊關係，並建立出模糊規則參數初始值，最後再用權重遞推最小二乘方法得到系統參數的最佳值，以建構出T-S模糊模型。
最後，我們使用本文的方法針對一些例子進行建模，並與其他方法做比較，經由實驗結果證明了本文提出的方法能更精準的建模。

關鍵字

T-S模糊模型 ； 模糊分群法 ； 粒子群演算法 ； 參數辨識

並列摘要

A novel modeling method for T-S fuzzy model is proposed in this thesis. Firstly, fuzzy c-means algorithm is adopted to classify the data points and determined the numbers of the cluster. In addition, by defining the cluster numbers as the rule number, several linear subsystems can be divided from unknown system. Moreover, particle swarm optimization (PSO) algorithm and fuzzy c-regression model (FCRM) algorithm are adopted to find the fuzzy relationship between the data points and these linear subsystems, and construct the initial value of the fuzzy rule parameters. Finally, the weight recursive least squares method is adopted to obtain the optimal values of the system parameters and establish the T-S fuzzy model. Some models are illustrated to demonstrate that our modeling method can provide the more precious model than some well-known methods.

並列關鍵字

T-S Fuzzy model ； Fuzzy Clustering ； Particle Swarm Optimization ； Parameter Identify

參考文獻

[1] J. Q. Chen and L. J. Chen, “An on-line identification algorithm for fuzzy systems,” Fuzzy Sets and Systems, vol.64, 1994, pp. 63-72.

[2] W. Pedrycz, “Identification algorithm in fuzzy relational systems,” Fuzzy Sets and Systems, vol.13, 1984, pp. 153-167.

[3] X. W. Xu and Z. Y. Lu, “Fuzzy model identification and self-learning for dynamic systems,” IEEE Transactions on systems, 1991, pp. 1320-1336.

[4] C. T. Sun, “Rule-based structure identification in an adaptive network based fuzzy inference system,” IEEE Transactions on Fuzzy Systems, 1994, pp. 64-73.

[5] S. Horikawa, T. Furuhashi and Y. Uchikawa, “On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm,” IEEE Transactions on Neural Networks, vol.3, 1992, pp. 801-806.