Parallel mining method for symbol application features of complex network images

Abstract

Aiming at the problems of poor denoising effect, low recognition rate and long feature mining time in current methods, a parallel feature mining method of image symbol application features in complex network based on neural network learning control is proposed. First, the standard deviation of image symbol noise is calculated and denoising is carried out by filtering parameters. Then the image symbol is segmented by using the two-dimensional maximum inter-group variance method. Finally, the momentum coefficient and learning efficiency are introduced to calculate the parameters of the neural network, and the improved simulated annealing algorithm is used to adjust the learning efficiency of the neural network, so as to realise the parallel mining of image symbol application features of the complex network. Experimental results show that this method has good image denoising effect, high image recognition rate and short feature mining time, which verifies the comprehensive effectiveness of this method.