摘要
首先介绍普通改进E lm an动态递归神经网络的结构,重点讨论一种具有P ID特性的E lm an神经网络及其学习算法,并将改进E lm an网络和P ID型E lm an网络分别用于动态系统的辨识.无论是理想的数学模型还是实际工业模型,计算机仿真结果均证明,将P ID型网络用于动态系统辨识具有更好的逼近效果.
A modified Elman dynamic recurrent neural network is introduced first. A PID Elman neural network and its learning algorithm are discussed in detail. Elman network and PID Elman network are used to identify the dynamical system, respectively. Simulation results based on ideal mathematical model and industrial model show that the PID Elman network is prior to the modified Elman network in identification of nonlinear dynamic systems.
出处
《控制与决策》
EI
CSCD
北大核心
2005年第10期1197-1200,共4页
Control and Decision