摘要
针对非线性系统逆模型的学习问题,提出一种基于贝叶斯-高斯神经网络(BGNN)的设计方法.BGNN模型的训练分为两个步骤,首先利用群智能优化算法进行BGNN的离线结构训练;然后用训练好的BGNN模型在线整合历史数据,进行非线性系统逆模型的获取.对水轮发电机组非线性系统进行了BGNN逆模型的仿真,结果表明了BGNN逆模型设计方法具有结构简单、在线辨识效果好等优点,适于非线性离散系统的逆模型设计.
A design method based on Bayesian-Gaussian neural network（BGNN） is proposed for the inverse modeling problem of nonlinear system.The training procedures of the BGNN model include two steps.The structure parameters of the BGNN model are optimized by using the swarm intelligence optimization algorithm.Furthermore,the trained BGNN model combining with the history data is applied to the on-line prediction of the nonlinear system inverse model.The inverse model simulation experiments are conducted on a nonlinear system of hydraulic turbine,and the results show that the inverse model design method based on BGNN has the easy topology determination and effective identification accuracy,and it is fit for nonlinear system inverse model design.
出处
《控制与决策》
EI
CSCD
北大核心
2010年第10期1567-1570,1579,共5页
Control and Decision
基金
国家自然科学基金项目(60704024
60772107)
江苏省普通高校自然科学研究计划项目(07KJD510109)
关键词
非线性
逆模型
贝叶斯-高斯神经网络
门槛矩阵
模型设计
Nonlinear system
Inverse model
Bayesian-Gaussian neural network
Threshold matrix
Model design
