函数链网络(Functional Link Network——FLN)通过对输入向量(或模式)的非线性扩展,将非线性映射特性引入了单层神经网络,采用δ学习规则获得了快速的学习和非线性映射特性。本文在FLN基础上,借助凸集优化思想,利用最陡梯度下降技术获...函数链网络(Functional Link Network——FLN)通过对输入向量(或模式)的非线性扩展,将非线性映射特性引入了单层神经网络,采用δ学习规则获得了快速的学习和非线性映射特性。本文在FLN基础上,借助凸集优化思想,利用最陡梯度下降技术获得了比FLN更高的存储容量和更快速的学习速度。计算机模拟的结果证实了所提的算法性能。展开更多
As for the factors affecting the heat transfer performance of complex and nonlinear oscillating heat pipe (OHP),grey relational analysis (GRA) was used to deal with the relationship between heat transfer rate of a loo...As for the factors affecting the heat transfer performance of complex and nonlinear oscillating heat pipe (OHP),grey relational analysis (GRA) was used to deal with the relationship between heat transfer rate of a looped copper-water OHP and charging ratio,inner diameter,inclination angel,heat input,number of turns,and the main influencing factors were defined.Then,forecasting model was obtained by using main influencing factors (such as charging ratio,interior diameter,and inclination angel) as the inputs of function chain neural network.The results show that the relative average error between the predicted and actual value is 4%,which illustrates that the function chain neural network can be applied to predict the performance of OHP accurately.展开更多
目前的宽度学习系统(Broad learning system,BLS)通过所建立的一系列映射节点和增强节点来形成联合节点。因为联合节点与输出层的线性连接,网络权值可以用求解伪逆的方法快速求得,避免了耗时的训练过程,从而成为快速而高效的学习方法。...目前的宽度学习系统(Broad learning system,BLS)通过所建立的一系列映射节点和增强节点来形成联合节点。因为联合节点与输出层的线性连接,网络权值可以用求解伪逆的方法快速求得,避免了耗时的训练过程,从而成为快速而高效的学习方法。然而在追求高精度结果的过程中,BLS对于增强节点数量的需求过于巨大,容易造成过拟合问题。为此,本文提出了基于函数链神经网络(Functional⁃link neural network,FLNN)的深度分类器(FLNN based deep classifier,FLNNDC),旨在提供一种更加简单却又不失精度的BLS变体结构。FLNNDC将几个轻量级的BLS子系统堆积成栈式结构,每一个轻量级的BLS子系统随机选择一部分映射节点生成增强节点,而不是全部映射节点。和原宽度结构相比,在几个主流数据集上的实验结果表明本文所提出的FLNNDC分类器具有网络结构更小且学习速度更快的优势。展开更多
文摘函数链网络(Functional Link Network——FLN)通过对输入向量(或模式)的非线性扩展,将非线性映射特性引入了单层神经网络,采用δ学习规则获得了快速的学习和非线性映射特性。本文在FLN基础上,借助凸集优化思想,利用最陡梯度下降技术获得了比FLN更高的存储容量和更快速的学习速度。计算机模拟的结果证实了所提的算法性能。
基金Project(531107040300) supported by the Fundamental Research Funds for the Central Universities in ChinaProject(2006BAJ04B04) supported by the National Science and Technology Pillar Program during the Eleventh Five-year Plan Period of China
文摘As for the factors affecting the heat transfer performance of complex and nonlinear oscillating heat pipe (OHP),grey relational analysis (GRA) was used to deal with the relationship between heat transfer rate of a looped copper-water OHP and charging ratio,inner diameter,inclination angel,heat input,number of turns,and the main influencing factors were defined.Then,forecasting model was obtained by using main influencing factors (such as charging ratio,interior diameter,and inclination angel) as the inputs of function chain neural network.The results show that the relative average error between the predicted and actual value is 4%,which illustrates that the function chain neural network can be applied to predict the performance of OHP accurately.
文摘目前的宽度学习系统(Broad learning system,BLS)通过所建立的一系列映射节点和增强节点来形成联合节点。因为联合节点与输出层的线性连接,网络权值可以用求解伪逆的方法快速求得,避免了耗时的训练过程,从而成为快速而高效的学习方法。然而在追求高精度结果的过程中,BLS对于增强节点数量的需求过于巨大,容易造成过拟合问题。为此,本文提出了基于函数链神经网络(Functional⁃link neural network,FLNN)的深度分类器(FLNN based deep classifier,FLNNDC),旨在提供一种更加简单却又不失精度的BLS变体结构。FLNNDC将几个轻量级的BLS子系统堆积成栈式结构,每一个轻量级的BLS子系统随机选择一部分映射节点生成增强节点,而不是全部映射节点。和原宽度结构相比,在几个主流数据集上的实验结果表明本文所提出的FLNNDC分类器具有网络结构更小且学习速度更快的优势。
