在对传统特征选择算法进行研究的基础上,提出了一种基于双模糊信息的特征选择算法(feature selection algorithm based on doubly fuzziness information,FSA-DFI)。第一种模糊体现在对最小学习机(least learning machine,LLM)进行模糊...在对传统特征选择算法进行研究的基础上,提出了一种基于双模糊信息的特征选择算法(feature selection algorithm based on doubly fuzziness information,FSA-DFI)。第一种模糊体现在对最小学习机(least learning machine,LLM)进行模糊化后得到模糊最小学习机(fuzzy least learning machine,FUZZYLLM)中;另一种模糊则是在基于贡献率模糊补充这一方法中体现的,其中贡献率高的特征才可能被选入最终的特征子集。算法FSA-DFI是将FUZZY-LLM和基于贡献率的模糊补充方法结合得到的。实验表明,和其他算法相比,所提特征选择算法FSA-DFI能得到更好的分类准确率、更好的降维效果以及更快的学习速度。展开更多
针对传统极限学习机预测滚动轴承故障时,存在信号模式混叠、人为参数选取造成预测精度低下的问题,提出了正态分布-经验小波变换变换结合偏最小二乘法的极限学习机(partial least squares-extreme learning machines,简称PLS-ELM)的故障...针对传统极限学习机预测滚动轴承故障时,存在信号模式混叠、人为参数选取造成预测精度低下的问题,提出了正态分布-经验小波变换变换结合偏最小二乘法的极限学习机(partial least squares-extreme learning machines,简称PLS-ELM)的故障预测方法。首先,提出正态分布经验小波变换信号降噪方法,通过正态分布划分频率带界限,在各频率带上构建带通滤波器进行降噪;其次,提出PLS-ELM的故障预测方法,应用偏最小二乘法(partial least squares,简称PLS)中主成分数和加载权重分别改进极限学习机(extreme learning machines,简称ELM)隐含层节点数和网络权值,激活函数选取Softmax以提高数据的拟合精度;最后,应用无量纲指标峭度来反映故障程度,实现故障趋势预测。试验结果表明,该方法能够准确划分频谱和克服模式混叠等问题,并实现滚动轴承性能衰退趋势预测。展开更多
At the early stages of deep-water oil exploration and development, fewer and further apart wells are drilled than in onshore oilfields. Supervised least squares support vector machine algorithms are used to predict th...At the early stages of deep-water oil exploration and development, fewer and further apart wells are drilled than in onshore oilfields. Supervised least squares support vector machine algorithms are used to predict the reservoir parameters but the prediction accuracy is low. We combined the least squares support vector machine （LSSVM） algorithm with semi-supervised learning and established a semi-supervised regression model, which we call the semi-supervised least squares support vector machine （SLSSVM） model. The iterative matrix inversion is also introduced to improve the training ability and training time of the model. We use the UCI data to test the generalization of a semi-supervised and a supervised LSSVM models. The test results suggest that the generalization performance of the LSSVM model greatly improves and with decreasing training samples the generalization performance is better. Moreover, for small-sample models, the SLSSVM method has higher precision than the semi-supervised K-nearest neighbor （SKNN） method. The new semi- supervised LSSVM algorithm was used to predict the distribution of porosity and sandstone in the Jingzhou study area.展开更多
To implement a real-time reduction in NOx,a rapid and accurate model is required.A PLS-ELM model based on the combination of partial least squares(PLS)and the extreme learning machine(ELM)for the establishment of the ...To implement a real-time reduction in NOx,a rapid and accurate model is required.A PLS-ELM model based on the combination of partial least squares(PLS)and the extreme learning machine(ELM)for the establishment of the NOx emission model of utility boilers is proposed.First,the initial input variables of the NOx emission model are determined according to the mechanism analysis.Then,the initial input data is extracted by PLS.Finally,the extracted information is used as the input of the ELM model.A large amount of real data was obtained from the distributed control system(DCS)historical database of a 1 000 MW power plant boiler to train and validate the PLS-ELM model.The modeling performance of the PLS-ELM was compared with that of the back propagation(BP)neural network,support vector machine(SVM)and ELM models.The mean relative errors(MRE)of the PLS-ELM model were 1.58%for the training dataset and 1.69%for the testing dataset.The prediction precision of the PLS-ELM model is higher than those of the BP,SVM and ELM models.The consumption time of the PLS-ELM model is also shorter than that of the BP,SVM and ELM models.展开更多
To obtain an accurate and robust soft sensor model in dealing with the increasingly complex industrial modeling data,an effective heterogeneous ensemble of extreme learning machines(HEELM)is proposed.Specifically,the ...To obtain an accurate and robust soft sensor model in dealing with the increasingly complex industrial modeling data,an effective heterogeneous ensemble of extreme learning machines(HEELM)is proposed.Specifically,the kernel extreme learning machine(KELM)and four common extreme learning machine(ELM)models that have different internal activations are contained in the HEELM for enriching the diversity of sub-models.The number of hidden layer nodes of the extreme learning machine is determined by the trial and error method,and the optimal parameters of the kernel extreme learning machine model are determined by cross validation.Moreover,to obtain the best output of the ensemble model,least squares regression is applied to aggregate the outputs of all individual models.Two complex data sets of practical industrial processes are used to test the HEELM performance.The simulation results show that the HEELM has a high prediction accuracy.Compared with the individual ELM models,bagging ELM ensemble model,BP and SVM models,the prediction accuracy of the HEELM model is improved by 4.5%to 8.7%,and the HEELM model can obtain better generalization capability.展开更多
Support Vector Machine (SVM) is a powerful methodology for solving problems in non-linear classification, function estimation and density estimation, which has also led to many other recent developments in kernel base...Support Vector Machine (SVM) is a powerful methodology for solving problems in non-linear classification, function estimation and density estimation, which has also led to many other recent developments in kernel based methods in general. This paper presents a highaccuracy and fault-tolerant SVM for the mobile geo-location problem, which is an important component of pervasive computing. Simulation results show its basic location performance, and illustrate impacts of the number of training samples and training area on test location error.展开更多
Generative adversarial network(GAN) is the most exciting machine learning breakthrough in recent years,and it trains the learning model by finding the Nash equilibrium of a two-player zero-sum game.GAN is composed of ...Generative adversarial network(GAN) is the most exciting machine learning breakthrough in recent years,and it trains the learning model by finding the Nash equilibrium of a two-player zero-sum game.GAN is composed of a generator and a discriminator,both trained with the adversarial learning mechanism.In this paper,we introduce and investigate the use of GAN for novelty detection.In training,GAN learns from ordinary data.Then,using previously unknown data,the generator and the discriminator with the designed decision boundaries can both be used to separate novel patterns from ordinary patterns.The proposed GAN-based novelty detection method demonstrates a competitive performance on the MNIST digit database and the Tennessee Eastman(TE) benchmark process compared with the PCA-based novelty detection methods using Hotelling's T^2 and squared prediction error statistics.展开更多
文摘在对传统特征选择算法进行研究的基础上,提出了一种基于双模糊信息的特征选择算法(feature selection algorithm based on doubly fuzziness information,FSA-DFI)。第一种模糊体现在对最小学习机(least learning machine,LLM)进行模糊化后得到模糊最小学习机(fuzzy least learning machine,FUZZYLLM)中;另一种模糊则是在基于贡献率模糊补充这一方法中体现的,其中贡献率高的特征才可能被选入最终的特征子集。算法FSA-DFI是将FUZZY-LLM和基于贡献率的模糊补充方法结合得到的。实验表明,和其他算法相比,所提特征选择算法FSA-DFI能得到更好的分类准确率、更好的降维效果以及更快的学习速度。
文摘针对传统极限学习机预测滚动轴承故障时,存在信号模式混叠、人为参数选取造成预测精度低下的问题,提出了正态分布-经验小波变换变换结合偏最小二乘法的极限学习机(partial least squares-extreme learning machines,简称PLS-ELM)的故障预测方法。首先,提出正态分布经验小波变换信号降噪方法,通过正态分布划分频率带界限,在各频率带上构建带通滤波器进行降噪;其次,提出PLS-ELM的故障预测方法,应用偏最小二乘法(partial least squares,简称PLS)中主成分数和加载权重分别改进极限学习机(extreme learning machines,简称ELM)隐含层节点数和网络权值,激活函数选取Softmax以提高数据的拟合精度;最后,应用无量纲指标峭度来反映故障程度,实现故障趋势预测。试验结果表明,该方法能够准确划分频谱和克服模式混叠等问题,并实现滚动轴承性能衰退趋势预测。
基金supported by the "12th Five Year Plan" National Science and Technology Major Special Subject:Well Logging Data and Seismic Data Fusion Technology Research(No.2011ZX05023-005-006)
文摘At the early stages of deep-water oil exploration and development, fewer and further apart wells are drilled than in onshore oilfields. Supervised least squares support vector machine algorithms are used to predict the reservoir parameters but the prediction accuracy is low. We combined the least squares support vector machine （LSSVM） algorithm with semi-supervised learning and established a semi-supervised regression model, which we call the semi-supervised least squares support vector machine （SLSSVM） model. The iterative matrix inversion is also introduced to improve the training ability and training time of the model. We use the UCI data to test the generalization of a semi-supervised and a supervised LSSVM models. The test results suggest that the generalization performance of the LSSVM model greatly improves and with decreasing training samples the generalization performance is better. Moreover, for small-sample models, the SLSSVM method has higher precision than the semi-supervised K-nearest neighbor （SKNN） method. The new semi- supervised LSSVM algorithm was used to predict the distribution of porosity and sandstone in the Jingzhou study area.
基金The National Natural Science Foundation of China(No.71471060)Natural Science Foundation of Hebei Province(No.E2018502111)
文摘To implement a real-time reduction in NOx,a rapid and accurate model is required.A PLS-ELM model based on the combination of partial least squares(PLS)and the extreme learning machine(ELM)for the establishment of the NOx emission model of utility boilers is proposed.First,the initial input variables of the NOx emission model are determined according to the mechanism analysis.Then,the initial input data is extracted by PLS.Finally,the extracted information is used as the input of the ELM model.A large amount of real data was obtained from the distributed control system(DCS)historical database of a 1 000 MW power plant boiler to train and validate the PLS-ELM model.The modeling performance of the PLS-ELM was compared with that of the back propagation(BP)neural network,support vector machine(SVM)and ELM models.The mean relative errors(MRE)of the PLS-ELM model were 1.58%for the training dataset and 1.69%for the testing dataset.The prediction precision of the PLS-ELM model is higher than those of the BP,SVM and ELM models.The consumption time of the PLS-ELM model is also shorter than that of the BP,SVM and ELM models.
基金The National Natural Science Foundation of China(No.71471060)the Natural Science Foundation of Hebei Province(No.E2018502111)Fundamental Research Funds for the Central Universities(No.2019QN134).
文摘To obtain an accurate and robust soft sensor model in dealing with the increasingly complex industrial modeling data,an effective heterogeneous ensemble of extreme learning machines(HEELM)is proposed.Specifically,the kernel extreme learning machine(KELM)and four common extreme learning machine(ELM)models that have different internal activations are contained in the HEELM for enriching the diversity of sub-models.The number of hidden layer nodes of the extreme learning machine is determined by the trial and error method,and the optimal parameters of the kernel extreme learning machine model are determined by cross validation.Moreover,to obtain the best output of the ensemble model,least squares regression is applied to aggregate the outputs of all individual models.Two complex data sets of practical industrial processes are used to test the HEELM performance.The simulation results show that the HEELM has a high prediction accuracy.Compared with the individual ELM models,bagging ELM ensemble model,BP and SVM models,the prediction accuracy of the HEELM model is improved by 4.5%to 8.7%,and the HEELM model can obtain better generalization capability.
文摘Support Vector Machine (SVM) is a powerful methodology for solving problems in non-linear classification, function estimation and density estimation, which has also led to many other recent developments in kernel based methods in general. This paper presents a highaccuracy and fault-tolerant SVM for the mobile geo-location problem, which is an important component of pervasive computing. Simulation results show its basic location performance, and illustrate impacts of the number of training samples and training area on test location error.
文摘Generative adversarial network(GAN) is the most exciting machine learning breakthrough in recent years,and it trains the learning model by finding the Nash equilibrium of a two-player zero-sum game.GAN is composed of a generator and a discriminator,both trained with the adversarial learning mechanism.In this paper,we introduce and investigate the use of GAN for novelty detection.In training,GAN learns from ordinary data.Then,using previously unknown data,the generator and the discriminator with the designed decision boundaries can both be used to separate novel patterns from ordinary patterns.The proposed GAN-based novelty detection method demonstrates a competitive performance on the MNIST digit database and the Tennessee Eastman(TE) benchmark process compared with the PCA-based novelty detection methods using Hotelling's T^2 and squared prediction error statistics.
