一种径向基函数神经网络预测在超临界温度控制系统中的应用研究

Application Research on Neural Network Predictive Control Based on GGAP-RBF for Supercritical Main Steam

超临界温度控制系统具有较大的惯性、时滞和非线性,且动态特性随运行工况而改变,难以建立其精确的数学模型,本文采用GGAP算法的RBF神经网络构成神经网络预测控制器,将在线学习和预测控制相结合,以某超临界电厂主汽温度为研究对象,MATLAB仿真实验表明,该方法能对超临界温度控制系统实现有效的控制,动态性能较传统的PID控制有较大的提高。

This paper presents a RBF（Redial Basis Function） neural network controller on superheat temperature system in supercritical units,a sequential algorithm for RBF networks referred to as the generalized growing and pruning algorithm for RBF（GGAP-RBF） is introduced and then uses it in the learning algorithm to realize parsimonious networks.The structure of this controller makes no need to use another neural network for on-line system identification and determining the structure of neural network controller in advance.The simulation for Super Heat Temperature control system using presented method is take out.The results show that the control system performance is better than the conventional PID control system.