基于T-S模型的自适应神经模糊推理系统及其在热工过程建模中的应用

ANFIS Modeling Based on T-S Model and Its Application for Thermal Process

在工业热工过程控制中,被控对象动态特性往往表现出非线性、时变性、大迟延和大惯性等特点,这使得难以对其建立比较精确的模型,从而难于精确表达热工过程及实施整体优化控制。针对热工过程建模难的现状,为达到建立精确非线性模型的目的,提出1种基于T-S模型的自适应神经模糊系统(ANFIS)模糊建模方法。该方法通过对模糊系统的结构辨识和参数辨识,使神经模糊网络能够自主、迅速有效地收敛到要求的输入和输出关系,从而达到精确建模的目的。仿真结果验证了所提出的算法的有效性,将其应用到热工过程建模中可获得高精度的非线性模型。

In the thermal process, the dynamic behavior of plants shows a characteristic of great delay, big inertia, time variance and non-linearity, which make the modeling very difficult, and so the whole optimal control for thermal processes is impossible. According to this actuality and the objective of building accurate nonlinear model for thermal process, an adaptive neural-fuzzy inference system（ANFIS） modeling method based on T-S model is proposed. Through the structure and parameter identification of fuzzy identification system, the system can converge to the required input-output relationships independently and rapidly. The effectiveness of the proposed fuzzy modeling method based on ANFIS is demonstrated by simulation results and the accurate nonlinear fuzzy models can be obtained when the method is applied to the thermal processes.