@Article{cmc.2020.06395,
AUTHOR = {Wei Fang, Feihong Zhang, Yewen Ding, Jack Sheng},
TITLE = {A New Sequential Image Prediction Method Based on LSTM and DCGAN},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {1},
PAGES = {217--231},
URL = {http://www.techscience.com/cmc/v64n1/39139},
ISSN = {1546-2226},
ABSTRACT = {Image recognition technology is an important field of artificial intelligence. 
Combined with the development of machine learning technology in recent years, it has 
great researches value and commercial value. As a matter of fact, a single recognition 
function can no longer meet people’s needs, and accurate image prediction is the trend 
that people pursue. This paper is based on Long Short-Term Memory (LSTM) and Deep 
Convolution Generative Adversarial Networks (DCGAN), studies and implements a 
prediction model by using radar image data. We adopt a stack cascading strategy in 
designing network connection which can control of parameter convergence better. This 
new method enables effective learning of image features and makes predictive models to 
have greater generalization capabilities. Experiments demonstrate that our network model 
is more robust and efficient in terms of timing prediction than 3DCNN and traditional 
ConvLSTM. The sequential image prediction model architecture proposed in this paper is 
theoretically applicable to all sequential images.},
DOI = {10.32604/cmc.2020.06395}
}