TY - GEN
T1 - Real-Time implementation of the coupled neural mass and its application
AU - Hao, Xinyu
AU - Li, Huiyan
AU - Wang, Jiang
AU - Wei, Xile
AU - Yang, Shuangming
AU - Che, Yanqiu
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/6/30
Y1 - 2018/6/30
N2 - The neural mass model is a self-oscillation network composed of two neural populations. In this study, we use the fieldprogrammable gate array (FPGA) device to implement the neural mass model and the hardware implementation results are exactly the same as the MATLAB simulation results. The study reveals that dynamical characteristics of the neural population implemented on FPGA can meet the real-Time computational requirements. Besides, we propose a control method of the robotic arm based on the oscillation dynamics of the network. For the implementation results of FPGA is real-Time, it can be used to realize the robotic control. A closed-loop control system is realized by inputting the error signals of robotic arm into the neural network model and obtaining the feedback signal to arm joint for error elimination. The results show that the control method based on the neural mass model can quickly and effectively eliminate the angle errors.
AB - The neural mass model is a self-oscillation network composed of two neural populations. In this study, we use the fieldprogrammable gate array (FPGA) device to implement the neural mass model and the hardware implementation results are exactly the same as the MATLAB simulation results. The study reveals that dynamical characteristics of the neural population implemented on FPGA can meet the real-Time computational requirements. Besides, we propose a control method of the robotic arm based on the oscillation dynamics of the network. For the implementation results of FPGA is real-Time, it can be used to realize the robotic control. A closed-loop control system is realized by inputting the error signals of robotic arm into the neural network model and obtaining the feedback signal to arm joint for error elimination. The results show that the control method based on the neural mass model can quickly and effectively eliminate the angle errors.
UR - http://www.scopus.com/inward/record.url?scp=85055885456&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85055885456&partnerID=8YFLogxK
U2 - 10.1145/3233740.3233749
DO - 10.1145/3233740.3233749
M3 - Conference contribution
AN - SCOPUS:85055885456
T3 - ACM International Conference Proceeding Series
SP - 29
EP - 34
BT - Proceedings of 2018 International Conference on Intelligent Science and Technology, ICIST 2018
PB - Association for Computing Machinery
T2 - 2018 International Conference on Intelligent Science and Technology, ICIST 2018
Y2 - 30 June 2018 through 2 July 2018
ER -