A New Sequential Image Prediction Method Based on LSTM and DCGAN

作     者：Wei Fang Feihong Zhang Yewen Ding Jack Sheng 

作者机构：School of Computer&SoftwareJiangsu Engineering Center of Network MonitoringNanjing University of Information Science&TechnologyNanjing210044China State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjing210023China Department of EconomicsFinanceInsurance and Risk Management University of Central ArkansasConway72035USA 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2020年第64卷第7期

页      面：217-231页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was supported in part by the Open Research Project of State Key Laboratory of Novel Software Technology under Grant KFKT2018B23 the Priority Academic Program Development of Jiangsu Higher Education Institutions,and the Open Project Program of the State Key Lab of CAD\&CG(Grant No.A1916),Zhejiang University 

主　　题：Image prediction LSTM DCGAN 

摘      要：Image recognition technology is an important field of artificial *** with the development of machine learning technology in recent years,it has great researches value and commercial *** a matter of fact,a single recognition function can no longer meet people’s needs,and accurate image prediction is the trend that people *** 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 *** adopt a stack cascading strategy in designing network connection which can control of parameter convergence *** new method enables effective learning of image features and makes predictive models to have greater generalization *** demonstrate that our network model is more robust and efficient in terms of timing prediction than 3DCNN and traditional *** sequential image prediction model architecture proposed in this paper is theoretically applicable to all sequential images.