Abstract:For the problem that convolutional kernels of typical convolutional neural network set by experience and being difficult for fixed network architecture to realize subsequent re-learning

a new Convoutional neural network (CNN) model was proposed based on Sparse Autoencoder(SAE) and typical CNN. By SAE pre-training convolution kernels and increasing a branch based on typical CNN

only branch weights of the model were updated while doing subsequent re-learning

and new features were added while memorizing existing characteristics. On dataset MNIST

recognition rate of 97.65% was achieved by updating weights for 10 times. Recognition rates of more than 93% were achieved on dataset HCL2000. The recognition rate of similar Chinese characters reached 80.36% using 50 samples for training

250 for testing. Compared with typical CNN and traditional methods

the proposed method was more generalized. Experiments show that the proposed method can be effectively applied to image recognition applications such as handwritten characters.