基于动态递归模糊神经网络的水轮发电机组建模  

Modelling for hydroelectric generating unit based on dynamic recurrent fuzzy neural network

作　　者：罗南华[1] 王伟[2] 

机构地区：[1]东北大学信息科学与工程学院 [2]大连理工大学信息与控制研究中心,辽宁大连116024

出　　处：《控制与决策》2007年第10期1125-1128,1133,共5页Control and Decision

基　　金：国家杰出青年基金项目(69825106);东北电力集团科研项目(99AQ-09)

摘　　要：针对水轮发电机组的复杂动态特征,提出一种新的动态递归模糊神经网络结构,并将其应用于解决水轮发电机组的建模问题.通过在网络规则层引入乘积运算,使得复杂模糊规则容易提取,模糊推理功能易于实现.在网络隐层中引入局部递归单元,该方法能克服基于反向误差传播的静态网络辨识动态系统的局限性,提高辨识水轮发电机组动态特性的准确性.通过与其他智能方法的仿真比较,验证了所提出方法的有效性.Aiming at complex dynamic characteristics of the hydroelectric generating unit, a novel dynamic recurrent fuzzy neural network is proposed, which is used to model the hydroelectric generating unit. The fuzzy inference function is realized easily by using a product operation in the network. Introducing local recurrent units to hidden layer, the proposed method can overcome the limit of using back-propagation-based static network methods and accurately identify the dynamic characteristics of the hydroelectric generating unit. Comparing with the traditional methods, the effectiveness of the proposed method is verified.

关 键 词：水轮发电机组 动态递归 模糊神经网络 动态反向传播学习算法 

分 类 号：TM312[电气工程—电机]