|
|
|
| Research on Disturbance Rejection of Adaptive Spiking Neural Network Based on Synaptic Plasticity under White Gaussian Noise |
| Guo Lei1,2, Liu Dongzhao1,2, Huang Fengrong3, Yu Hongli1,2 |
1. State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin 300130 China;
2. Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province Hebei University of Technology Tianjin 300130 China;
3. School of Mechanical Engineering Hebei University of Technology Tianjin 300130 China |
|
|
|
|
Abstract With the electromagnetic environment becoming more and more complex, the shortcomings of traditional anti-electromagnetic disturbance methods are becoming increasingly prominent. The organism under the regulation of nervous system has the advantages on self-organization, self-adaption and disturbance rejection. It is significant to explore a new method of anti-electromagnetic disturbance based on the excellent characteristics of organism. Therefore, a new field of electromagnetic bionic protection emerged. In this paper, the disturbance rejection of the spiking neural network based on excitatory synaptic plasticity and inhibitory synaptic plasticity was analyzed. A ten-layer feed-forward spiking neural network was constructed. The firing rate and correlation between membrane potential of the neuron were considered as indexes for assessing disturbance rejection ability. The experimental results show that: under a certain intensity of white Gaussian noise disturbance, the relative variation of output firing rate is tiny; the correlation between membrane potential in output layer is relatively large. It is concluded that the spiking neural network based on synaptic plasticity has a certain ability to reject noise disturbance. This study lays the theoretical foundation for improving the protection ability of electronic system in the complex electromagnetic environment.
|
|
Received: 31 October 2018
Published: 17 January 2020
|
|
|
| Fund:This work is supported by the National Natural Science Foundation of China (61571180, 51877068). |
|
Corresponding Authors:
Guo Lei male, born in 1968, Ph.D, Professor, Doctoral supervisor, major research interests include neural engineering and brain networks. E-mail: guoshengrui@163.com
|
| About author:: Liu Dongzhao male, born in 1992, Doctoral candidate, major research interests include neural engineering and brain networks.E-mail: 1164531355@qq.com |
|
|
|
[1] Jing Wuwei, Huang Xueliang, Chen Chen, et al.Study on impacts among wireless power transmission multi- system[J]. Transactions of China Electrotechnical Society, 2015, 30(14): 457-462.
[2] Que Weiyan, Sun Yongquan, Liang Jingxiu, et al.Military system E3 test and evaluation during acquisition life cycle[J]. High Power Laser and Particle Beams, 2014, 26(7): 073202.
[3] Zhou Hong, Jiang Yan, Hu Wenshan, et al.Review and research on health and safety issues for magnetically- coupled resonant wireless power transfer systems[J]. Transactions of China Electrotechnical Society, 2016, 31(2): 1-12.
[4] Ju Zhengquan, Man Menghua, Yuan Liang.Design and reliability analysis of self organizing fault- tolerant system[J]. Microelectronics and Computer, 2012, 29(4): 89-93.
[5] Liu Shanghe, Yuan Liang, Chu Jie.Electromagnetic bionics-a new study field of electromagnetic protection[J]. Natural Journal, 2009, 31(1): 1-7.
[6] Chang Xiaolong, Zhao Guoliang, Wu Cuixia, et al.Research progress and prospect on study of bio- inspired electromagnetic protection technology[J]. Computer Engineering and Design, 2013, 34(4): 1517-1521.
[7] Yuan Liang, Man Menghua, Chang Xiaolong.Principles of electromagnetic protection bionics and research of fault self-recovery mechanism[J]. Engineering Sciences, 2014, 16(3): 77-85.
[8] Song Wanshan, Guo Wei, Zhang Yulian, et al.Progress of relevant research on the material basis of synaptic plasticity[J]. Chinese Journal of Gerontology, 2015, 35(17): 5039-5042.
[9] Chen Weiheng, Tao Changlu, Shi Meiyu, et al.Synaptic plasticity and brain disease[J]. Chinese Bulletin of Life Sciences, 2014, 26(6): 583-585.
[10] Liu Yichun, Xu Haiyang, Wang Zhongqiang, et al.Research on synaptic bionic devices based on memristor[J]. Science, 2013, 65(2): 21-25.
[11] Chen Yunzhi, Xu Guizhi, Zhou Qian, et al.Modelling and simulation of adaptive neural network based on spike-timing dependent plasticity[J]. Chinese Journal of Medical Physics, 2014, 31(2): 4820-4824.
[12] Wei Yi, Koulakov A A.Long-term memory stabilized by noise-induced rehearsal[J]. Journal of Neuro- science, 2014, 34(47): 15804-15815.
[13] Ruan Chengmei, Liu Chibiao, Qiu Jinming.Stability of a spiking neural networks with STDP learning algorithm[J]. Journal of Yulin University, 2017, 27(6): 95-98.
[14] Chang Xiaolong, Ding Guoliang, Lou Jianan.Anti- disturbance of neuron network synchronization[J]. Journal of Shanghai Jiaotong University, 2014, 48(10): 1485-1490.
[15] Wang Meili, Wang Junsong.Dynamical balance between excitation and inhibition of feedback neural circuit via inhibitory synaptic plasticity[J]. Acta Physica Sinica, 2015, 64(10): 108701.
[16] Zhang Hui, Lu Wenbing, Zhao Xiongwen, et al.Noise modeling for power line communication channel using the LS-SVM and wavelet neural networks[J]. Transactions of China Electrotechnical Society, 2018, 33(1): 3879-3888.
[17] Pei Yu, Chen Yuanming, Bian Xiaoyang, et al.Non- dispersion infrared SF6 gas sensor with air pressure compensation based on RBF neural network[J]. Journal of Applied Optics, 2018, 39(3): 366-372.
[18] Izhikevich E M.Simple model of spiking neurons[J]. IEEE Transactions on Neural Networks, 2003, 14(6): 1569-1572.
[19] Kenneth A L, Chris F, Liu C, et al.Dynamical analysis of Parkinsonian state emulated by hybrid Izhikevich neuron models[J]. Communications in Nonlinear Science and Numerical Simulation, 2015, 28(11): 10-26.
[20] Hu Junying, Zhang Jiangshe, Zhang Chunxia, et al.A new deep neural network based on a stack of single-hidden-layer feedforward neural networks with randomly fixed hidden neurons[J]. Neurocomputing, 2016, 171: 63-72.
[21] Le T N, Bao P T, Huynh H T.Liver tumor segmentation from MRI using 3D fast marching algorithm and single hidden layer feedforward neural network[J]. Biomed Research International, 2016, 2016(43): 1-8.
[22] Vogels T P, Sprekeler H, Zenke F, et al.Inhibitory plasticity balances excitation and inhibition in sensory pathways and memory networks[J]. Science, 2011, 334(6062): 1569-1573.
[23] Li Jianxuan, Zhao Zhihua, Zhang Xiangming, et al.Measurement technology of high frequency disturbance current of the electric energy conversion device in the electromagnetic launch system[J]. Transactions of China Electrotechnical Society, 2018, 33(24): 5805-5810. |
|
|
|
2020, Vol. 35 
