Faculty of Electrical Engineering Tržaška 25, Ljubljana, Slovenia.
ISA Trans. 2011 Apr;50(2):159-69. doi: 10.1016/j.isatra.2011.01.004. Epub 2011 Feb 2.
In this paper we propose a new approach to on-line Takagi-Sugeno fuzzy model identification. It combines a recursive fuzzy c-means algorithm and recursive least squares. First the method is derived and than it is tested and compared on a benchmark problem of the Mackey-Glass time series with other established on-line identification methods. We showed that the developed algorithm gives a comparable degree of accuracy to other algorithms. The proposed algorithm can be used in a number of fields, including adaptive nonlinear control, model predictive control, fault detection, diagnostics and robotics. An example of identification based on a real data of the waste-water treatment process is also presented.
在本文中，我们提出了一种新的在线 Takagi-Sugeno 模糊模型辨识方法。它结合了递归模糊 c-均值算法和递归最小二乘法。首先推导了该方法，然后在麦基-格拉斯时间序列的基准问题上对其进行了测试和比较，并与其他已建立的在线辨识方法进行了比较。结果表明，所提出的算法可以与其他算法达到相当的精度。该算法可用于自适应非线性控制、模型预测控制、故障检测、诊断和机器人等多个领域。还介绍了基于废水处理过程实际数据的辨识示例。
