针对循环神经网络(recurrent neural network,RNN)的结构不易确定、参数学习过程复杂等问题,提出一种增量构造式随机循环神经网络(incremental-construction for random RNN,IRRNN),实现了RNN结构的增量构造与参数的随机学习.首先建立...针对循环神经网络(recurrent neural network,RNN)的结构不易确定、参数学习过程复杂等问题,提出一种增量构造式随机循环神经网络(incremental-construction for random RNN,IRRNN),实现了RNN结构的增量构造与参数的随机学习.首先建立隐含节点增量构造的约束机制,同时利用候选节点池策略实现隐含节点的优选,避免了网络随机构造的盲目性;进一步,从模型参数的局部优化与全局优化两个角度考虑,提出模型参数的两种增量随机(incremental random,IR)学习方法,即IR-1与IR-2,并证明了其万能逼近特性;同时通过研究IRRNN的动态特性,分析了IRRNN的泛化性能.通过实验验证了IRRNN在动态特性、紧凑性和精度等多个方面具有良好特性.展开更多
Direct online measurement on product quality of industrial processes is difficult to be realized,which leads to a large number of unlabeled samples in modeling data.Therefore,it needs to employ semi-supervised learnin...Direct online measurement on product quality of industrial processes is difficult to be realized,which leads to a large number of unlabeled samples in modeling data.Therefore,it needs to employ semi-supervised learning(SSL)method to establish the soft sensor model of product quality.Considering the slow time-varying characteristic of industrial processes,the model parameters should be updated smoothly.According to this characteristic,this paper proposes an online adaptive semi-supervised learning algorithm based on random vector functional link network(RVFLN),denoted as OAS-RVFLN.By introducing a L2-fusion term that can be seen a weight deviation constraint,the proposed algorithm unifies the offline and online learning,and achieves smoothness of model parameter update.Empirical evaluations both on benchmark testing functions and datasets reveal that the proposed OAS-RVFLN can outperform the conventional methods in learning speed and accuracy.Finally,the OAS-RVFLN is applied to the coal dense medium separation process in coal industry to estimate the ash content of coal product,which further verifies its effectiveness and potential of industrial application.展开更多
文摘针对污水处理复杂系统中关键水质参数生化需氧量(biochemical oxygen demand,BOD)难以准确实时预测的问题,在分析污水处理过程相关影响因素的基础上,提出一种基于敏感度分析法的自组织随机权神经网络(selforganizing neural network with random weights,SONNRW)软测量方法.该方法首先通过机理分析选取原始辅助变量,经过数据预处理,之后采用主元分析法对辅助变量进行精选,作为SONNRW的输入变量进行污水处理关键水质参数BOD的预测.SONNRW算法利用隐含层节点输出及其权值向量计算该隐含层节点对于残差的敏感度,根据敏感度大小对网络隐含层节点进行排序,删除敏感度较低的隐含层节点即冗余点.仿真结果表明:该软测量方法对水质参数BOD的预测精度高、实时性好、模型结构稳定,能够用于污水水质的在线预测.
文摘针对循环神经网络(recurrent neural network,RNN)的结构不易确定、参数学习过程复杂等问题,提出一种增量构造式随机循环神经网络(incremental-construction for random RNN,IRRNN),实现了RNN结构的增量构造与参数的随机学习.首先建立隐含节点增量构造的约束机制,同时利用候选节点池策略实现隐含节点的优选,避免了网络随机构造的盲目性;进一步,从模型参数的局部优化与全局优化两个角度考虑,提出模型参数的两种增量随机(incremental random,IR)学习方法,即IR-1与IR-2,并证明了其万能逼近特性;同时通过研究IRRNN的动态特性,分析了IRRNN的泛化性能.通过实验验证了IRRNN在动态特性、紧凑性和精度等多个方面具有良好特性.
基金Projects(61603393,61973306)supported in part by the National Natural Science Foundation of ChinaProject(BK20160275)supported by the Natural Science Foundation of Jiangsu Province,China+1 种基金Projects(2015M581885,2018T110571)supported by the Postdoctoral Science Foundation of ChinaProject(PAL-N201706)supported by the Open Project Foundation of State Key Laboratory of Synthetical Automation for Process Industries of Northeastern University,China
文摘Direct online measurement on product quality of industrial processes is difficult to be realized,which leads to a large number of unlabeled samples in modeling data.Therefore,it needs to employ semi-supervised learning(SSL)method to establish the soft sensor model of product quality.Considering the slow time-varying characteristic of industrial processes,the model parameters should be updated smoothly.According to this characteristic,this paper proposes an online adaptive semi-supervised learning algorithm based on random vector functional link network(RVFLN),denoted as OAS-RVFLN.By introducing a L2-fusion term that can be seen a weight deviation constraint,the proposed algorithm unifies the offline and online learning,and achieves smoothness of model parameter update.Empirical evaluations both on benchmark testing functions and datasets reveal that the proposed OAS-RVFLN can outperform the conventional methods in learning speed and accuracy.Finally,the OAS-RVFLN is applied to the coal dense medium separation process in coal industry to estimate the ash content of coal product,which further verifies its effectiveness and potential of industrial application.
