differential parameter meaning in Chinese

微分参数

In this paper , to resolve the coupling phenomena between temperature and humidity in wood drying system , a bp neural network based pid controller is proposed and applied to wood drying system . the architecture and learning algorithm of the proposed controller is more simpler and the physical meanings of the input layer ' s neurons and output layer ' s neurons are explicit . based on predefined control rules and self - learning , the bp network changs the scaling integral and differential parameters , therefore is able to control the variants using classical pid control algorithms and at the same time , decoupling control is implemented as well during the control procedure
本文针对木材干燥过程中温、湿度耦合的现象，提出一种将新的基于bp神经网络的pid控制器应用于木材干燥控制系统的方案，其结构和学习算法相对简单，输入层和输出层神经元物理意义明确；它根据设定的某一控制规律，通过网络的自学习，调整pid控制器的比例、积分和微分参数，从而利用经典的pid控制算法得到相应各变量的控制量参与控制，并在该过程中实现解耦控制，而不用给定样本信号进行在线的学习。