基于长短期记忆与TOPSIS的深度递归学习
Deep Recurrent Learning Through Long Short Term Memory and TOPSIS
AI总结：
本文将云ERP采用建模为深度递归神经网络问题，提出结合LSTM与TOPSIS的分类排序算法来识别采用特征，并通过参考模型验证和用户定性调查形成关键采用因素假设。
摘要：
企业资源规划（ERP）软件将资源和数据整合在一起，以维持公司业务流程中的软件流转。然而，云计算廉价、简便且快速的管理承诺推动企业所有者从单体架构向基于数据中心/云端的ERP过渡。由于云ERP开发涉及一个循环过程，即规划、实施、测试和升级，其采用被建模为一个深度递归神经网络问题。最终，提出了一种基于长短期记忆（LSTM）和TOPSIS的分类算法，分别用于识别和排序采用特征。我们的理论模型通过阐明关键参与者、服务、架构和功能，在一个参考模型上进行了验证。通过考虑技术、创新和阻力问题，在用户中开展了定性调查，以形成关于关键采用因素的假设。
英文摘要：
Enterprise resource planning (ERP) software brings resources, data together to keep software-flow within business processes in a company. However, cloud computing's cheap, easy and quick management promise pushes business-owners for a transition from monolithic to a data-center/cloud based ERP. Since cloud-ERP development involves a cyclic process, namely planning, implementing, testing and upgrading, its adoption is realized as a deep recurrent neural network problem. Eventually, a classification algorithm based on long short term memory (LSTM) and TOPSIS is proposed to identify and rank, respectively, adoption features. Our theoretical model is validated over a reference model by articulating key players, services, architecture, functionalities. Qualitative survey is conducted among users by considering technology, innovation and resistance issues, to formulate hypotheses on key adoption factors.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
