忆阻Fourier神经网络在图像复原中的应用

Applications of Memristive Fourier Neural Network in Image Restoration

将传统Fourier神经网络与忆阻器相结合,用忆阻器做突触,构建新型的忆阻Fourier神经网络.推导忆导变化与权值更新的关系,提出忆阻突触权值更新规则,构建单输入忆阻Fourier神经网络,提出忆阻BP算法对模糊二值图像和灰度进行处理.Matlab仿真实验表明该算法可以有效实现图像复原,提高图像清晰度.忆阻Fourier神经网络有望用于解决复杂的图像处理问题.

A new memristive Fourier neural network was proposed based on the combination of the tradi tional Fourier neural network and a memristor, a new circuit element, synapse. The relationship between memristive conductance and synapse the weight updating rule of the memristive synapse was proposed. Th with the memristor acting as the weight updating was derived, and e single input memristive Fourier neural network was built, and a memristive BP algorithm was used to process blurred binary and gray ima ges. Matlab simulation confirmed that the memristive BP algorithm proposed could realize image restora tion and heighten the legibility of images. The memristive Fourier neural network could be used for sol ving complex image processing problems.