Fractional Calculus Numerical Algorithm based on Artificial Intelligence

Fractional calculus has been widely cited in various fields today, especially in the field of image denoising studied in this article. However, due to its early birth, this algorithm inevitably faces some computational stability and accuracy issues. Therefore, this article took image denoising as the perspective and optimized the algorithm by using artificial intelligence technology. The specific method of this article is to simplify and analyze the fractional Lorentz system based on the convolutional neural network model in deep learning, and then verify the ability of the convolutional neural network model through comparative testing on image denoising ability. The fractional calculus numerical algorithm based on convolutional neural network model was set as the experimental group, while the conventional fractional calculus numerical algorithm was set as the control group. The specific test results under 100 images showed that the PSNR and SSIM of the control group were 20.24 and 0.65, while the PSNR and SSIM of the experimental group were 28.64 and 0.92, respectively. The experimental group showed significantly stronger performance in both PSNR and SSIM than the control group. Therefore, this study found that convolutional neural networks can significantly improve the ability of fractional calculus, the experiment on peak signal-to-noise ratio and structural similarity in this article illustrates this point.