摘要
针对热力系统的非线性特性,采用RBF神经网络进行被控对象的动态特性模型辨识,设计了评价模糊控制器控制性能的FITAE指标;采用遗传算法优化模糊控制器参数,来优化比例因子和控制规则表,并采用二阶段优化策略:首先只优化比例因子,得到一组次优的模糊控制器参数;然后优化比例因子和控制规则表,得到优化的模糊控制器参数。
Considering the non-liner feather of thermal system, it adopt identification model based on the RBF neuron network, design a FITAE index to appraise the controlling performance of fuzzy controller. Adopt GA to optimize the fuzzy controller including proportion factor and form of controlling regulation. To pick up speed of fuzzy controller self-adaptation, adopt the two stages optimize strategy. Firstly, optimize the proportion factor only. Secondly, optimize the proportion factor and the form of controlling regulation; get a fuzzy controller optimized.
出处
《电站系统工程》
北大核心
2003年第4期42-44,共3页
Power System Engineering
基金
国家经贸委最佳节能项目(case study 30)
