基于模拟正交神经网络的电热干燥器温度控制

该文研究的目的是建立一种模拟正交神经网络控制器用于电热干燥器的温度控制。首先在数字正交神经网络的基础上给出模拟神经网络的学习算法，然后提出模拟正交神经网络加积分的并行控制方法，并应用于电热干燥器的温度控制中。温度控制仿真结果证明，这种控制器比PID控制器具有更好的快速性和较小的超调，温度控制获得了满意的控制效果。该模拟神经控制器能用于不确定对象的控制，为不确定系统控制提供了一种新的途径。

Temperature control of electric heating dryers based on an analog orthogonal neural network

The purpose of this investigation is to establish a controller of analog orthogonal neural network for the temperature control over electric heating dryers. First, a learning algorithm of the analog neural network was given on the basis of the digital orthogonal neural network. Then a parallel control method with the analog neural network and integrator was presented and was applied in the temperature control over electric heating dryers. The simulation results applied in the temperature control verify that this controller has higher speed learning performance and smaller overshoot than a PID controller. The temperature control obtains satisfactory control effectiveness. The analog neural controller is suitable for the control over uncertain objects and provides a novel approach for a type of uncertain control system.