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PID-adjusting precise control method of functional electrical stimulation (FES) based on BP neural network

A BP neural network, functional electrical stimulation technology, applied in neural learning methods, biological neural network models, electrotherapy and other directions to achieve the effect of improving accuracy and stability

Inactive Publication Date: 2009-12-09
TIANJIN UNIV
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Problems solved by technology

Although the closed-loop control is used to automatically adjust the FES stimulation intensity and timing parameters according to the deviation between the action

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  • PID-adjusting precise control method of functional electrical stimulation (FES) based on BP neural network
  • PID-adjusting precise control method of functional electrical stimulation (FES) based on BP neural network
  • PID-adjusting precise control method of functional electrical stimulation (FES) based on BP neural network

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Embodiment Construction

[0028] The present invention proposes a new method of adaptively optimizing the proportional, integral and differential coefficients of the PID to precisely control functional electrical stimulation parameters by means of an error back-propagation (BP) neural network. Its technical process is to optimize the proportional coefficient, differential coefficient and integral coefficient of PID through self-learning of artificial neural network and self-adjustment of weighting coefficients, and then control the current pulse intensity of FES system. This method is a brand-new precise control technology of functional electrical stimulation.

[0029] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0030] The structure of the application of the precise control method of functional electrical stimulation based on BP neural network tuning PID is as follows: figure 1 shown. The neural network tuning proces...

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Abstract

The invention belongs to the technical field of rehabilitation devices, relating to artificial neural networks and the precise control of functional electrical stimulation (FES), in particular to a PID-adjusting FES precise control method based on BP neural network. In order to improve the accuracy and stability of an FES system, the invention adopts a technical proposal as follows: first assigning weighting coefficient initial values for each layer and selecting proper learning rate eta; then taking initial output value yout, input and output error and preset input value rin as the input values of the BP neural network; calculating the final output values of the neural network, namely coefficients Kp, Ki and Kd of PID; calculating the output value yout and error between the output value and the input value of the system under new PID coefficients and then carrying out next step of self-learning and weighting coefficient self-adjusting of the neural network; repeating the processes to finally realize on-line adjusting of self-adaptation of the PID control parameters and using the PID control parameters in the FES system. The method is primarily used for FES control.

Description

technical field [0001] The invention belongs to the technical field of rehabilitation equipment, and relates to an artificial neural network and precise control of functional electrical stimulation, in particular to a precise control method of functional electrical stimulation based on BP neural network to adjust PID. Background technique [0002] Functional electrical stimulation (Functional Electrical Stimulation, FES) stimulates limb motor muscles and peripheral nerves through current pulse sequences, which can effectively restore or rebuild part of the motor function of paraplegic patients. According to statistics, the number of paralyzed patients with spinal cord injury is increasing year by year. Due to the weak regeneration ability of the spinal cord, there is currently no effective treatment method that can directly repair the injury, and only functional rehabilitation training can be implemented. In the 1960s, Liberson used electrical stimulation of the peroneal ner...

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Application Information

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IPC IPC(8): A61N1/08A61N1/36G05B13/02G06N3/02G06N3/08G06N3/04
Inventor 程龙龙张广举明东万柏坤綦宏志
Owner TIANJIN UNIV
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