An approach to the control and synchronization of the scalar chaotic signal by means of neural networks with linear outputs is presented.
本文给出了一种利用线性输出神经网络实现标量混沌信号同步控制的方法。
The approximate controller gives the rough control and the neural network controller gives the complementary signal to further reduce the output tracking error.
常规控制器对系统给出粗略控制，神经网络控制器给出补偿信号来进一步减小系统输出跟踪误差。
Because fuzzy neural network is simple and practical, it has been widely used in industrial process control, signal processing and other fields.
模糊神经网络由于其简单实用，已被广泛用于工业过程控制、信号处理等领域。
It was applied in the control system of prosthesis hand, the detected signal was recognized by neural network, 6 action patterns can be classified, the success rate is above 95%.
将其应用于假肢手的控制系统中，通过神经网络进行动作模式识别，共识别了6个手部动作模式，识别成功率在95%以上。
Neural networks controller is trained under supervised signal when system works at extreme control mode and there is no supervised signal when system works at distal control mode.
极值控制时神经网络进行有监督的学习，远程控制时神经网络进行无监督的学习。
Neural networks controller is trained under supervised signal when system works at extreme control mode and there is no supervised signal when system works at distal control mode.
极值控制时神经网络进行有监督的学习，远程控制时神经网络进行无监督的学习。
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