The Volterra feedforward neural network with nonlinear interconnections and related homotopy learning algorithm are proposed in the paper. It is shown that Volterra neural network and the homolopy learning algorithms ...The Volterra feedforward neural network with nonlinear interconnections and related homotopy learning algorithm are proposed in the paper. It is shown that Volterra neural network and the homolopy learning algorithms are significant potentials in nonlinear approximation ability,convergent speeds and global optimization than the classical neural networks and the standard BP algorithm, and related computer simulations and theoretical analysis are given too.展开更多
This paper introduces a self-learning control approach based on approximate dynamic programming. Dynamic programming was introduced by Bellman in the 1950's for solving optimal control problems of nonlinear dynami...This paper introduces a self-learning control approach based on approximate dynamic programming. Dynamic programming was introduced by Bellman in the 1950's for solving optimal control problems of nonlinear dynamical systems. Due to its high computational complexity, the applications of dynamic programming have been limited to simple and small problems. The key step in finding approximate solutions to dynamic programming is to estimate the performance index in dynamic programming. The optimal control signal can then be determined by minimizing (or maximizing) the performance index. Artificial neural networks are very efficient tools in representing the performance index in dynamic programming. This paper assumes the use of neural networks for estimating the performance index in dynamic programming and for generating optimal control signals, thus to achieve optimal control through self-learning.展开更多
A quasi physical algorithm was proposed for solving the linear separation problem of point set in n dimensional space.The original idea of the quasi physical algorithm is to find an equivalent physical world for the p...A quasi physical algorithm was proposed for solving the linear separation problem of point set in n dimensional space.The original idea of the quasi physical algorithm is to find an equivalent physical world for the primitive mathematical problem and to observe the vivid images of the motion of matter in it so as to be inspired to obtain an algorithm for solving the mathematical problem. In this work, the electrostatics with two kinds of matter is found to be the equivalent physical world. As a result,the proposed algorithm is evidently more efficient and robust than the famous LMS algorithm and ETL algorithm. The efficiency of the quasi physical algorithm is about 10-50 times of the LMS algorithm’s for representative instances. A typical Boolean valued instance shows that it is hard for ETL algorithm but very easy for the quasi physical algorithm.In this instance, point set A and B is {000, 010, 011, 111} and {001,100}, respectively.展开更多
In this paper, a wavelet based fuzzy neural network for interval estimation of processed data with its interval learning algorithm is proposed. It is also proved to be an efficient approach to calculate the wavelet c...In this paper, a wavelet based fuzzy neural network for interval estimation of processed data with its interval learning algorithm is proposed. It is also proved to be an efficient approach to calculate the wavelet coefficient.展开更多
乙烯裂解炉是乙烯生产的核心装置,烃类原料在裂解炉中发生复杂的高温裂解反应,及时识别裂解炉运行工况变化对设备安全高效运行非常重要。裂解炉运行过程中产生大量的过程数据,这些数据通常具有多变量、高维度特性,增加了数据处理和分析...乙烯裂解炉是乙烯生产的核心装置,烃类原料在裂解炉中发生复杂的高温裂解反应,及时识别裂解炉运行工况变化对设备安全高效运行非常重要。裂解炉运行过程中产生大量的过程数据,这些数据通常具有多变量、高维度特性,增加了数据处理和分析的复杂性,如何基于过程数据及时检测乙烯裂解炉工况变化成为亟需解决的问题。借鉴对比学习算法在图片分类中的优秀性能,提出一类基于对比学习的裂解炉运行工况识别方法。首先,将乙烯裂解炉工业数据经归一化后,使用不同长度的时间窗动态提取数据,将其转化为灰度图片。根据图片中的信息,将图片进行数据增强后输入编码器,得到图片的全局语义、类别、内容不变性等特征。将这些特征应用于计算对比学习的损失函数,通过最小化对比损失函数,实现对灰度图片的分类。通过本文方法,可以根据过程数据快速发现工况变化,其分类准确度较通用时间序列表示学习的自监督对比学习(self-supervised contrastive learning for universal time series representation learning,TimesURL)方法有明显提升,可有效实现乙烯裂解炉工况识别。展开更多
精准预测高速铁路风险对高速铁路安全管理至关重要。为有效预测高速铁路运行中的风险概率,解决事故诱因内外部特征的提取与学习过程难以同时兼顾的问题,提出一种考虑事故诱因拓扑结构的内外双视角的高速铁路风险预测模型(internal and e...精准预测高速铁路风险对高速铁路安全管理至关重要。为有效预测高速铁路运行中的风险概率,解决事故诱因内外部特征的提取与学习过程难以同时兼顾的问题,提出一种考虑事故诱因拓扑结构的内外双视角的高速铁路风险预测模型(internal and external perspectives on the topological dendrogram of accident causes,IEPTDAC)。首先,基于树状结构刻画事故内部诱因的拓扑关系,从“人、机、环、管”4个方面提取事故诱因的外部特征;在此基础上,采用卷积神经网络的多层卷积操作提取事故诱因的内外部特征,并引入粒子群算法对卷积神经网络的关键超参数进行优化,进一步提升模型的预测性能;最后,选取某铁路局的5个区段,以19个事故诱因与风险事故数据作为研究对象,在1、3和5 h的时间粒度下,分别采用9种既有预测模型与IEPTDAC模型进行对比分析。实验结果表明,相较于现有的组合预测模型以及传统的单一预测模型,IEPTDAC模型拥有更优的预测精度和拟合效果。例如,在1 h时间粒度下,对比实验中基于暂态提取变换与DSRNet-AttBiLSTM的预测模型,IEPTDAC模型的平均绝对误差fmae降低了32.04%,均方根误差f_(rmse)降低了36.35%,决定系数f_(r^(2))提高了0.46%;在1、3和5 h的时间粒度下,IEPTDAC与传统的ConvLSTM(convolutional long short-term memory)模型相比,f_(r^(2))分别提高1.71%、3.00%、1.27%。此外,本文设计的模型消融实验验证了IEPTDAC模型各分支的合理性和有效性。该方法为高速铁路风险预测提供了一种有效的技术手段。展开更多
文摘The Volterra feedforward neural network with nonlinear interconnections and related homotopy learning algorithm are proposed in the paper. It is shown that Volterra neural network and the homolopy learning algorithms are significant potentials in nonlinear approximation ability,convergent speeds and global optimization than the classical neural networks and the standard BP algorithm, and related computer simulations and theoretical analysis are given too.
基金Supported by the National Science Foundation (U.S.A.) under Grant ECS-0355364
文摘This paper introduces a self-learning control approach based on approximate dynamic programming. Dynamic programming was introduced by Bellman in the 1950's for solving optimal control problems of nonlinear dynamical systems. Due to its high computational complexity, the applications of dynamic programming have been limited to simple and small problems. The key step in finding approximate solutions to dynamic programming is to estimate the performance index in dynamic programming. The optimal control signal can then be determined by minimizing (or maximizing) the performance index. Artificial neural networks are very efficient tools in representing the performance index in dynamic programming. This paper assumes the use of neural networks for estimating the performance index in dynamic programming and for generating optimal control signals, thus to achieve optimal control through self-learning.
基金TheNationalKeyBasicResearchProgram (973) (No .G 19980 30 6 0 0 )
文摘A quasi physical algorithm was proposed for solving the linear separation problem of point set in n dimensional space.The original idea of the quasi physical algorithm is to find an equivalent physical world for the primitive mathematical problem and to observe the vivid images of the motion of matter in it so as to be inspired to obtain an algorithm for solving the mathematical problem. In this work, the electrostatics with two kinds of matter is found to be the equivalent physical world. As a result,the proposed algorithm is evidently more efficient and robust than the famous LMS algorithm and ETL algorithm. The efficiency of the quasi physical algorithm is about 10-50 times of the LMS algorithm’s for representative instances. A typical Boolean valued instance shows that it is hard for ETL algorithm but very easy for the quasi physical algorithm.In this instance, point set A and B is {000, 010, 011, 111} and {001,100}, respectively.
文摘In this paper, a wavelet based fuzzy neural network for interval estimation of processed data with its interval learning algorithm is proposed. It is also proved to be an efficient approach to calculate the wavelet coefficient.
文摘乙烯裂解炉是乙烯生产的核心装置,烃类原料在裂解炉中发生复杂的高温裂解反应,及时识别裂解炉运行工况变化对设备安全高效运行非常重要。裂解炉运行过程中产生大量的过程数据,这些数据通常具有多变量、高维度特性,增加了数据处理和分析的复杂性,如何基于过程数据及时检测乙烯裂解炉工况变化成为亟需解决的问题。借鉴对比学习算法在图片分类中的优秀性能,提出一类基于对比学习的裂解炉运行工况识别方法。首先,将乙烯裂解炉工业数据经归一化后,使用不同长度的时间窗动态提取数据,将其转化为灰度图片。根据图片中的信息,将图片进行数据增强后输入编码器,得到图片的全局语义、类别、内容不变性等特征。将这些特征应用于计算对比学习的损失函数,通过最小化对比损失函数,实现对灰度图片的分类。通过本文方法,可以根据过程数据快速发现工况变化,其分类准确度较通用时间序列表示学习的自监督对比学习(self-supervised contrastive learning for universal time series representation learning,TimesURL)方法有明显提升,可有效实现乙烯裂解炉工况识别。
文摘精准预测高速铁路风险对高速铁路安全管理至关重要。为有效预测高速铁路运行中的风险概率,解决事故诱因内外部特征的提取与学习过程难以同时兼顾的问题,提出一种考虑事故诱因拓扑结构的内外双视角的高速铁路风险预测模型(internal and external perspectives on the topological dendrogram of accident causes,IEPTDAC)。首先,基于树状结构刻画事故内部诱因的拓扑关系,从“人、机、环、管”4个方面提取事故诱因的外部特征;在此基础上,采用卷积神经网络的多层卷积操作提取事故诱因的内外部特征,并引入粒子群算法对卷积神经网络的关键超参数进行优化,进一步提升模型的预测性能;最后,选取某铁路局的5个区段,以19个事故诱因与风险事故数据作为研究对象,在1、3和5 h的时间粒度下,分别采用9种既有预测模型与IEPTDAC模型进行对比分析。实验结果表明,相较于现有的组合预测模型以及传统的单一预测模型,IEPTDAC模型拥有更优的预测精度和拟合效果。例如,在1 h时间粒度下,对比实验中基于暂态提取变换与DSRNet-AttBiLSTM的预测模型,IEPTDAC模型的平均绝对误差fmae降低了32.04%,均方根误差f_(rmse)降低了36.35%,决定系数f_(r^(2))提高了0.46%;在1、3和5 h的时间粒度下,IEPTDAC与传统的ConvLSTM(convolutional long short-term memory)模型相比,f_(r^(2))分别提高1.71%、3.00%、1.27%。此外,本文设计的模型消融实验验证了IEPTDAC模型各分支的合理性和有效性。该方法为高速铁路风险预测提供了一种有效的技术手段。
