基于突触可塑性的SNN随钻陀螺仪漂移处理

针对随钻振动引起MEMS陀螺仪的数据漂移问题,文中提出了一种脉冲神经网络算法.首先根据陀螺仪漂移误差的时间特性,利用脉冲网络的脉冲时间编码陀螺仪的信息强度.然后利用Izhikevich神经元模型的突触可塑性,调节激发性突触电导并抑制性突触电导,增强网络的鲁棒性,从而提高陀螺仪信号对噪声的抗干扰能力.在不同振动频率下,分...

Drift Processing of Gyro While Drilling Based on Synaptic Plasticity Pulsed Neural Network

In view of the data drift problem of MEMS gyroscope caused by vibration while drilling, a spiking neural network algorithm is proposed in this study. First, according to the time characteristics of the drift error of the gyroscope, the pulse time of the spiking neural network is used to encode the information intensity of the gyroscope. Then, the synaptic plasticity of the Izhikevich neuron model is used to adjust the excitatory synaptic conductance and inhibitory synaptic conductance to enhance the robustness of the network, thereby improving the anti-interference ability of the gyroscope signal against noise. Finally, under different vibration frequencies, the correlation between the firing rate of the Gaussian white noise output neuron and the membrane potential is analyzed. Experimental results show that under strong vibrations of different frequencies, noise has little effect on the firing rate of output neurons and the relative change of firing rate of output layer neurons, and has little effect on the membrane potential of output layer neurons, but has a greater impact on the correlation between membrane potentials. These results indicate that the proposed method improves the anti-interference ability of the gyroscope under vibration and noise, and can provide a new idea for the processing of gyroscope drift.