Application of A Neural Network Predictive Control Based on GGAP-RBF for the Supercritical Main Steam

摘要：

The Supercritical Main Steam has a large inertia, delay and nonlinear and dynamic characteristics change with the operating conditions, it is difficult to establish the precise mathematical model, this algorithm based on RBF neural network GGAP posed a direct neural network predictive controller, the combination of online learning and control to a supercritical power plant main steam temperature as the research object, MATLAB simulation results show that the superheated steam temperature system can achieve effective control, performance than the conventional PID control has greatly improved.

关键词： Radial basis function neural network global approximation super critical power plant main steam temperature

作者: Yun-Juan Li Yan-jun Fang Qi Li

作者单位: Kunming University Kunming 650118, China Wuhan University Wuhan 430072, China Naval University of engineeing Wuhan 430033, China

会议类型: 国际会议

会议名称: 2010 International Conference on Software and Computing Technology(2010年软件与计算机技术国际会议 ICSCT 2010)

会议地点: 昆明

会议语种:英文

页码: 367-370

在线出版日期: 2010-10-17（万方平台首次上网日期，不代表论文的发表时间）