The control system can adjust the weights of hybrid neural network and the parameters of controller timely to keep good control performance when the character of controlled plant varies.
当被控对象的特性发生变化时，可对混合神经网络权值及时进行修正并调整控制器参数使控制系统始终保持良好的控制性能。
Finally, this paper presents an improved model of the magnetic chain, using a single neural network PID controller to adjust the stator flux, and set up the corresponding flux estimation model.
最后本论文提出了一种改进的磁链模型，引入了单神经元网络PID控制器来调节定子磁链，并建立了相应的磁链模型。
To BP neural shortcoming of network, study one dynamic self-adaptation is it study improvement type BP algorithm of parameter to adjust.
针对BP神经网络的缺点，研究了一种动态自适应调整学习参数的改进型BP算法。
By using this model, people need not select any fuzzy logic in advance, and can adjust the network structure by the structure and parameter learning of the neural network.
该模型无需事先确定模糊控制规则，并能通过神经网络的结构及参数学习调整模糊神经网络的结构。
A kind of variable structure neural network algorithm is adopted to adjust fuzzy rules, and improves the ability of self-studying and self-adjusting in fuzzy control rules.
应用一种变结构神经网络算法对初始化的模糊规则进行调整，提高模糊控制规则的自学习和自适应能力。
Moreover, T-S model is used to adjust the dynamic fuzzy rules by the latter neural network, which can improve the adaptability of the control system.
采用T - S模型，由后件网络动态调整模糊规则，提高控制系统的适应性。
It can not only reduce the network learning cycle, but also optimize the network structure by using Kalman filter to adjust of the parameters of the neural network.
利用卡尔曼滤波调整神经网络的参数，不仅可以减少网络的学习周期，而且可以优化网络的结构。
As long as the camera had a clear view of the bin and arm, the neural network would be able to adjust and continue learning to pick up objects.
只要相机能够清楚地拍到容器和手臂，神经网络便可以进行调整，继续学习抓取物品。
For the shortages of the method in which network flux is predicted by forward neural network based on traditional BP algorithm, a 2-level network flux predict-adjust model is constructed.
针对基于传统BP算法的前向神经网络预测网络流量方法的不足，构建了一种二级的网络流量预测-校正模型。
For the shortages of the method in which network flux is predicted by forward neural network based on traditional BP algorithm, a 2-level network flux predict-adjust model is constructed.
针对基于传统BP算法的前向神经网络预测网络流量方法的不足，构建了一种二级的网络流量预测-校正模型。
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