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- W2388745100 abstract "According to the request of ultra-high precision control for stage of STM,a new control scheme based on neural network is proposed.The mathematical model is set up by analyzing the principle of the actuator.Mechanical parameters are identified on-line by the neural identifier.The traditional PID controller is replaced by a neural network self-learning PID controller. The on-line collection of sample is realized.The disadvantage of controller's off-line training is overcome.The weights of BP network and the parameters of neural net PID controller can be adjusted by the functions of self-learning and adaptability.The performance of real control system is enhanced.The results of experiment show that the response time is shortened from 3.8 s to 1.8 s,and the stable error is reduced from 4.2% to 1.9% under a displacement of 10 μm." @default.
- W2388745100 created "2016-06-24" @default.
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- W2388745100 date "2006-01-01" @default.
- W2388745100 modified "2023-09-25" @default.
- W2388745100 title "Neural Network Control of Scanning Tunneling Microscope Precision Stage" @default.
- W2388745100 hasPublicationYear "2006" @default.
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