Inverse Problem of Optic Process Tomography Solved by Using Linear Neural Networks
用线性神经网络映射光学过程层析成像的逆问题

The inverse problem of process tomography is also named imaging algorithms, it not only agree with the interactional principle of the stimulating field and measured substance, also match the spatial array geometry of sensors. The performance of proposed imaging algorithms(including image quality and calculating interval per frame) is a key question whether the process tomography can be applied to the industry process monitoring and control system. In order to get excellent reconstructing image, a sort of Linear Neural Networks method for image reconstruction are proposed. Through building the linear models of forward and inverse problems to optical tomography, this algorithms calculate the forward problem firstly to obtain pairs of modes of image projections relation, then which are used to train and build the Linear Neural Networks; Finally, the inverse problem can be reflected through using the trained linear neural networks. The numerical simulation demonstrated that the method is a robust imaging algorithm, the quality of image is excellent, and the temporal performance of imaging is very good with this method.