Deep learning is bringing revolution to pattern recognition and machine learning

which has been successfully applied to language processing

image processing

signal processing

business economy and so on.Restricted Boltzmann machine (RBM) is a strong representation and generative mod el

however

the learning time of deep belief nets (DBN)

which consists of multiple stacking RBM

will be longer.In this paper

the improved momentum method is used not only in gradient ascent algorithm but also in gradient descent algorithm for both classification accuracy enhancement and training time decreasing.According to the characteristics of the gradient ascent algorithm

a rapidly ascending momentum method is used in the RBM pre-training phase

which greatly improves the speed of learning.According to the characteristics of the gradient descent algorithm

an improved slowly descending momentum term is also used in the fine-tuning stage to accurately find the best point.Through the recognition experiments on the MNIST dataset and CMU-PIE face dataset

the achieved results show that the improved momentum algorithm can effectively enhance the ability of image feature expression and improve both accuracy and computation efficiency.