胶囊内窥镜无线遥测定位的校正

为了进一步提高采用交流励磁定位无线跟踪胶囊内窥镜的定位精度,减小系统误差,提出了改进的神经网络定位校正方法。首先,设计了适应于胶囊内窥镜定位校正的神经网络结构;然后,采用Levenberg-Marquart算法结合贝叶斯正则化方法改进校正网络,抑制校正网络的过拟合。通过定位实验平台,建立了定位目标的跟踪位置与实际位置的样本对照数据表,并应用校正网络对定位数据进行校正。定位校正实验表明,改进的神经网络校正法可进一步减小定位误差,校正后的X,Y,Z,α,β分量的平均误差分别减小至8.7 mm,10.1 mm,7.3 mm,0.086 rad和0.081 rad。与基本BP算法相比,采用Levenberg-Marquart贝叶斯正则化的改进算法有效提高了定位校正网络的泛化能力和收敛精度。

Calibration method for wirelessly localizing capsule endoscopy

In order to non-invasively track a capsule endoscopy in the gastrointestinal tract, a telemetric localization method using alternating magnetic fields was presented. Focusing on the method, a Bayesian-regularization neural network based on the Levenberg-Marquart algorithm was investigated to reduce system errors. Firstly, the neural network structure for localization calibration was designed. Then, both Bayesian-regularization and Levenberg-Marquart algorithms were used to train the neural network to limit an over-fitting. Using an experimental platform for localization, both the calibration table for training the network and the validation table for verifying the calibration quality were established,and the location data were calibrated by the trained neural network. The calibration experiment shows that the proposed neural network can be trained well enough to efficiently compensate the errors in electromagnetic localizing system. The mean errors of X, Y, Z, α, β respectively have been reduced to 8.7 mm,10.1 mm,7.3 mm,0.086 rad and 0.081 rad after calibration. Comparing with the standard Back-Propagation(BP) algorithm, the Bayesian-regularization neural network based on Levenberg-Marquart algorithm has better performance in the generalization capability and convergence precision.