Image mining algorithm based on neural network learning and control
PAN Ming-bo
Abstract：Aiming at the situation that the convergence speed of traditional neural network algorithm is slow,the oscillation may appear in the learning process,and even the algorithm may converge to the local minimum value, an image classification recognition method based on wavelet transform fusion neural network was proposed. The Gaussian wavelet basis function was used to replace the hidden node function in the hidden layer of neural network. The network weight parameters in the learning process were adaptively adjusted with the wavelet neural network parameter initialization method and the improved simulated annealing algorithm. Therefore, such problem as the low learning efficiency of neural network can be solved. The results show that the correct classification and recognition rate of the proposed algorithm for five kinds of animal images is 84.0%, which increases by 4.2% and 6.1% than that of traditional neural network and sparse representation,respectively.
Keywords：wavelet transformneural networkimage miningimage classificationGaussian wavelet basissimulated annealing algorithmconnection weightCifar dataset
Publication Date：2018-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 322-327 )