中文摘要：

为了准确反映热工过程动态特性,实现热工过程整体优化控制,提出了一类新的径向基函数神经网络（RBF-NN）的建模方法：采用熵方法和竞争学习算法,结合非线性自回归滑动平均（NARMA）模型的输入/输出结构实现RBF-NN的优化,辨识RBF-NN结构,并用最小二乘算法（LS）确定权向量,实现了典型的非线性热工过程建模。通过两个实例验证：基于NARMA结构的RBF-NN建模,具有较高的辨识精度和较少的隐层节点。

英文摘要：

In order to accurately reflect the dynamic behavior and realize the whole optimal control of the thermal process, a novel modeling method of the RBF-NN （Radial Basis Function Neural Networks） model is proposed to build nonlinear model. This method is based on entropy clustering and competitive learning algorithm, combined with nonlinear autoregressive moving average （NARMA） model to identify the RBF- NN stucture, and the power vector is gotten by the least square algorithm. Two simulation experiments show that the proposed method of the identification based on NARMA model and RBF-NN can accurately describe the non-linearity of the process and has less hidden nodes.