【摘要】 针对目前电容层析成像系统图像重建分辨率不高,精确度低的问题,提出了一种新的采用Chebyshev神经网络对电容层析成像系统进行图像重建的方法。该神经网络不仅扩大了网络辨识模型的能力与学习适应性,而且算法简单,学习收敛速度快,有线性、非线性逼近精度高等优异特性。通过对封闭管道的气固两相流进行数据检测,并采用改进后的神经网络算法进行图像重建,实验结果证明该方法能明显改善成像质量,进而证明了该方法的有效性。更多还原

【Abstract】 In view of the low precision of the reconstruction image of Electrical Capacitance Tomography(ECT) at present, a new method of image reconstruction algorithm based on Chebyshev neural net works for electrical capacitance tomography is proposed.This neural network not only expands the identification ability and learning adaptation of the neural network,but also has a simple algorithm,a high speed convergence of learning process,and excellent characteristics in the linear and nonlinear accurate approximation.The system uses 12-electrode capacitance tomography system for gas-solid flow tube closure data detection,uses the improved neural network algorithm for image reconstruction,the obtained experimental results show that the method can improve the reconstruction image quality and testify the effectiveness of the proposed method.更多还原