The method of establishing on-line temperature prediction model, set point model and parameter tracing self-learning model of computerized heat recovery processing control system is introduced.
介绍了余热处理计算机控制系统的在线温度预报模型、设定模型和参数跟踪自学习模型的建立方法。
The PID controller based on BP neural networks is designed to realize control parameter self-learning and self-adjusting.
设计了基于BP神经网络的PID控制器，实现PID控制器参数自学习、自整定。
A new type of adaptive PID controller using diagonal recurrent neural network (DRNN) is presented. An on-line learning algorithm based on PID parameter self-tuning method is given.
提出了一种基于对角回归神经网络的PID控制器结构，给出了PID参数在线自整定的学习控制算法。
A parameter self-learning algorithm is presented after defining data structure and variable array to improve the prototype's adaptability to different size of workpieces.
在定义了数据结构和变量数组的基础上，给出了参数自学习过程算法，改善了模型样机对不同规格样本工件的适应性。
Simultaneously, if the system temperature is in a temporary state of stability, perfects the parameter U0 based on the self-learning algorithm.
同时不断判断系统温度是否处于暂时稳定状态，如果是，则启动自学习算法，对U0进行修正。
Simultaneously, if the system temperature is in a temporary state of stability, perfects the parameter U0 based on the self-learning algorithm.
同时不断判断系统温度是否处于暂时稳定状态，如果是，则启动自学习算法，对U0进行修正。
应用推荐