动态系统神经网络结构的改进及其应用

MODIFICATION OF NEURAL NETWORK STRUCTURE OF DYNAMIC SYSTEM AND ITS APPLICATION

在对动态神经网络结构分析的基础上,提出了新的网络结构模式,把具有同类特性的输人参数集中在一个节点上以代表该类参数的性质、产生这类参数对网络的综合作用。这样构造的更合乎逻辑的神经网络的权值总数要比传统的BP网络的权值总数大大减少,从而加快网络学习速度,有利于网络的在线学习和提高网络的可靠性与稳定性。此外,本文对神经网络逆动态控制器进行了分析,提出在输入层增加系统偏差作为一个输入变量,从而增强了控制器的控制质量和控制反应能力。最后,应用上述技术对CSTR典型化工实例进行了验证,取得了较好的结果。

The relationship between input and output of a dynamic system is analysed and a new - type structure of neural networks is proposed in this paper. There is a specific layer added in the network which is the first hidden layer. Each node in the first hidden layer only receives the input having the same property and produces an integrated signal. The network, as compared with the conventional network, is greatly reduces the number of weights. So it is of great benefit to speed up the training network and to improve the stability and convergence of the network. Then the inverse dynamic neural network controller has been discussed. It is suggested to add a control deviation in the input layer to improve the model accuracy of the controller. Finally, the effects of the structures above mentioned are demonstrated by using CSTR dynamic simulation and control.