摘要
由一名新闻记者-机器人与机器学习日报的工作人员新闻编辑新闻-一项关于数学的新研究现在可用。从起源看新闻报道中华人民共和国太原，Newsrx记者，研究称，"为了解决非平稳信号关键信息识别与测量能力不足问题本文在物流运输、状态检测、故障诊断等实际工业领域进行了研究提出了一种基于变分模态分解的关键信息识别和获取方法(VMD)、常规神经网络(CNNs)、长短期记忆网络(LSTM)和suppORT向量机(SVM)。首先,利用VMD和线性矩阵重构非平稳信号相关性分解。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews – A new study on Mathematics is now available. Acco rding to news reporting originating inTaiyuan, People’s Republic of China, by N ewsRx journalists, research stated, “In order to address theissue of insufficie nt ability to identify and measure the key information of non-stationary signals collectedin practical industrial fields such as logistics transportation, stat e detection, and fault diagnosis, this paperproposes a method to identify and m easure the key information based on variational mode decomposition(VMD), convol utional neural networks (CNNs), long short-term memory (LSTM) networks, and support vector machine (SVM). First, the non-stationary signal is reconstructed by u sing VMD and linearcorrelation decomposition.”
NETL
NSTL