介绍将PID控制与神经网络相结合,利用Davidon最小二乘法训练用于辨识和控制的神经网络,由此推导出对非线性对象仍有效的神经网络PID自校正控制器,还进行了仿真研究,并将其与常规的PID控制作了比较. This paper introduces PID control and neural network, uses Davidon least square method to train the neural network used for identification and control, and then derives a neural network PID self - tuning controller which is still effective for nonlinear objects. It also makes a simulation study, It is compared with the conventional PID control.