【作者】 梅鹍鹏；

【导师】 黄仙；

【作者基本信息】 华北电力大学（北京） ， 控制理论与控制工程， 2004， 硕士

【摘要】 锅炉是火力电站的重要设备之一。电站锅炉设备庞大、复杂；过程多变量、大延迟、强耦合。其控制和优化问题一直是这一领域学者所关注和研究的重点。 本文选取RBF神经网络作为建模工具，使用了数据预处理法和改进的最临近聚类算法等多种方法，增强了神经网络的泛化能力。 本文使用锅炉运行数据建立过程模型。通过改进的复合形优化算法，寻找最优输入变量组合，实现多目标函数的优化。 在本文最后编写了锅炉燃烧过程优化应用软件。更多还原

【Abstract】 Boilers are important components in power systems. Boiler is quite complex and huge in size. The process happened in it is of multi-variables and the parameters are heavily coupled together. So its control and optimization are targeted as one of the most key problems by researchers.In this paper, RBF Neural Network was selected as a tool for modeling according to the characteristics of boiler systems. Introducing ’Data-Preprocessing’ method and ’Improved Nearest Neighbor-Clustering’ algorithm to enhance the generalizing ability of RBF Neural Networks and thus improving its applicability.The boiler system was modeled through its static operation data. ’Multi-Complex’ Method was selected as the implementation of multi-goal optimization, and was used in optimization of the process based on the model gained, searching for the optimal input parameters.In the end of this paper, the software of Boiler Combustion System Operation Optimization was development.更多还原