基于RBF神经网络和单神经元PID的PMSM速度控制器

为提高PMSM(Permanent Magnet Synchronous Motor,永磁同步电机)伺服系统的控制水平,本文提出一种基于RBF(Radial Basis Function,径向基函数)神经网络和单神经元PID的PMSM速度控制器,可根据PMSM伺服系统的实时状态进行速度控制器内部参数自整定,由于具备这种能力,使PMSM伺服系统的速度环实现全局最优控制。实际中,工业过程控制对平稳度要求较高,针对此要求对单神经元PID算法进行改进,将固定比例系数改进为跟随系统误差变化的变比例系数,这种改进有效的减少了系统超调,使控制更加平稳。

电气传动系统智能控制的基本问题

系统阐述了电气传动系统的智能控制这一目前研究热点的理论概念和基本问题 。

Application of single neuron adaptive PID controller during the process of timber drying

The paper presents a method of using single neuron adaptive PID control for adjusting system or servo system to implement timber drying process control, which combines the thought of parameter adaptive PID control and the character of neural network on exactly describing nonlinear and uncertainty dynamic process organically. The method implements functions of adaptive and self-learning by adjusting weighting parameters. Adaptive neural network can make some output trail given hoping value to decouple in static state. The simulation result indicates the validity, veracity and robustness of the method used in the timber drying process

Design and Simulation of Single Neuron Adaptive PID Controller

The purpose of this paper is to design a neuron adaptive PID controller based on the theory of intelligent control of the extens- ive research on the characteristics of neuronss, neurons and PID controller. Artificial neurons have the adaptive, parallel processing, selflearning learning, and mare fault-tolerant characteristics. When the artificial neurons are used to control the process, the syste^n will enabled to en-sure that the accused has strong anti-interference capability and ro. bustness.

改进EEMD-GRU混合模型在径流预报中的应用

为解决径流预测模型存在的预测精确度低、稳定性差、延时高等问题,结合门控制循环单元神经网络(gated recurrent unit,GRU),集合经验模态分解(ensemble empirical mode decomposition,EEMD)的各自优点,提出一种基于改进EEMD方法的深度学习模型(EEMD-GRU)。该模型首先以智能算法对径流信号进行边界拓延,以解决EEMD边界效应。然后利用改进EEMD方法将径流信号分解为若干稳态分量,将各分量作为GRU模型的输入并对其进行预测。实验结果表明,与结合了经验模态分解的支持向量回归模型相比,并行EEMDGRU径流预测模型的预测精准度、可信度和效率分别提高82.50%、144.67%和95.49%。基于EEMD-GRU的最优运算结果表明,该方法可进一步减少区域防洪的经济损失,提高灾害监管的工作效率。