Three Dimensional Image Reconstruction of Electrical Capacitance Tomography Based on Improved ALEXNET Convolutional Neural Network

Abstract

A method is proposed that the corresponding AlexNet neural network is trained according to the data of different flow patterns for the problem of slow sample training and low imaging accuracy for the threedimensional image reconstruction algorithm of convolutional neural networks. The input data is classified by SVM according to the flow pattern, and the corresponding AlexNet convolution neural network is trained for singleclass sample data, which make the input data type simple of the neural network, the number of samples and the neural network is small. The AlexNet convolutional neural network uses the Adam algorithm with impulse and adaptive learning rate which can reduce the error oscillation and accelerate the convergence of neural networks during training. By comparing the imaging results of the improved AlexNet convolutional neural network and the LBP algorithm, it shows that the optimized AlexNet has a significant improvement in imaging accuracy and speed.

Keywords

• electrical capacitance tomography
• three-dimensional image reconstruction
• alexnet convolutional neural network
• adam gradient descent algorithm