A Vector Network Analyzer(VNA)can be used to identify oscillation frequency of a signal source with moderate or low Radio Frequency(RF)power if certain care is taken according to experimental results.Unlike reported i...A Vector Network Analyzer(VNA)can be used to identify oscillation frequency of a signal source with moderate or low Radio Frequency(RF)power if certain care is taken according to experimental results.Unlike reported in the literature that a resonant peak of measured absolute value of reflection coefficient greater than 1 that corresponds to an oscillation frequency,we report that by observing the magnitude change of one-port reflection coefficient across the entire swept frequency range,a sudden peak or a dip corresponds to an oscillation frequency,this is more accurate than other reports.In addition,using modern VNA as a signal detection method can significantly reduce measurement time and increase measurement accuracy to VNA capability for developing emerging signal generating devices at early stage,especially for planar,large quantity and operating in a wide frequency range.展开更多
As a simple,fast,and non-destructive measuring technology,dielectric spectroscopy is usually used to analyze the dielectric properties of agricultural products and food,and then to predict the main components of mater...As a simple,fast,and non-destructive measuring technology,dielectric spectroscopy is usually used to analyze the dielectric properties of agricultural products and food,and then to predict the main components of materials.However,the large and expensive vector network analyzers(VNA)with expensive analysis software applied in measuring dielectric properties make research limited to the laboratory.To acquire dielectric spectra in situ,a model for solving relative complex permittivity was derived,and its performance was validated.Then,a low-cost portable dielectric spectrometer with a mini VNA,a Raspberry Pi,and a coaxial probe as core parts was developed over the frequency range of 100-3000 MHz.The stability and accuracy of the developed spectrometer were tested using milk and juice.The results indicated that the relative errors of the model were within±5%for dielectric constant(ε′)and loss factor(ε″).The coefficients of variation of measuredε′andε″by the developed spectrometer at 100-3000 MHz were less than 1%and 2%,respectively.Compared with the dielectric properties obtained by using a commercial dielectric measurement system,the relative errors of measuredε′andε″were within±3.4%and±6.0%,respectively.This study makes fast,non-destructive,and on-site food quality detection using dielectric spectra possible.展开更多
Molten iron temperature as well as Si, P, and S contents is the most essential molten iron quality （MIQ） indices in the blast furnace （BF） ironmaking, which requires strict monitoring during the whole ironmaking p...Molten iron temperature as well as Si, P, and S contents is the most essential molten iron quality （MIQ） indices in the blast furnace （BF） ironmaking, which requires strict monitoring during the whole ironmaking production. However, these MIQ parameters are difficult to be directly measured online, and large-time delay exists in off-line analysis through laboratory sampling. Focusing on the practical challenge, a data-driven modeling method was presented for the prediction of MIQ using the improved muhivariable incremental random vector functional-link net- works （M-I-RVFLNs）. Compared with the conventional random vector functional-link networks （RVFLNs） and the online sequential RVFLNs, the M-I-RVFLNs have solved the problem of deciding the optimal number of hidden nodes and overcome the overfitting problems. Moreover, the proposed M I RVFLNs model has exhibited the potential for multivariable prediction of the MIQ and improved the terminal condition for the multiple-input multiple-out- put （MIMO） dynamic system, which is suitable for the BF ironmaking process in practice. Ultimately, industrial experiments and contrastive researches have been conducted on the BF No. 2 in Liuzhou Iron and Steel Group Co. Ltd. of China using the proposed method, and the results demonstrate that the established model produces better estima ting accuracy than other MIQ modeling methods.展开更多
Vector neural network(VNN)is one of the most important methods to process interval data.However,the VNN,which contains a great number of multiply-accumulate(MAC)operations,often adopts pure numerical calculation metho...Vector neural network(VNN)is one of the most important methods to process interval data.However,the VNN,which contains a great number of multiply-accumulate(MAC)operations,often adopts pure numerical calculation method,and thus is difficult to be miniaturized for the embedded applications.In this paper,we propose a memristor based vector-type backpropagation(MVTBP)architecture which utilizes memristive arrays to accelerate the MAC operations of interval data.Owing to the unique brain-like synaptic characteristics of memristive devices,e.g.,small size,low power consumption,and high integration density,the proposed architecture can be implemented with low area and power consumption cost and easily applied to embedded systems.The simulation results indicate that the proposed architecture has better identification performance and noise tolerance.When the device precision is 6 bits and the error deviation level(EDL)is 20%,the proposed architecture can achieve an identification rate,which is about 92%higher than that for interval-value testing sample and 81%higher than that for scalar-value testing sample.展开更多
Accurate cost estimation at the early stage of a construction project is key factor in a project’s success. But it is difficult to quickly and accurately estimate construction costs at the planning stage, when drawin...Accurate cost estimation at the early stage of a construction project is key factor in a project’s success. But it is difficult to quickly and accurately estimate construction costs at the planning stage, when drawings, documentation and the like are still incomplete. As such, various techniques have been applied to accurately estimate construction costs at an early stage, when project information is limited. While the various techniques have their pros and cons, there has been little effort made to determine the best technique in terms of cost estimating performance. The objective of this research is to compare the accuracy of three estimating techniques (regression analysis (RA), neural network (NN), and support vector machine techniques (SVM)) by performing estimations of construction costs. By comparing the accuracy of these techniques using historical cost data, it was found that NN model showed more accurate estimation results than the RA and SVM models. Consequently, it is determined that NN model is most suitable for estimating the cost of school building projects.展开更多
Option pricing has become one of the quite important parts of the financial market. As the market is always dynamic, it is really difficult to predict the option price accurately. For this reason, various machine lear...Option pricing has become one of the quite important parts of the financial market. As the market is always dynamic, it is really difficult to predict the option price accurately. For this reason, various machine learning techniques have been designed and developed to deal with the problem of predicting the future trend of option price. In this paper, we compare the effectiveness of Support Vector Machine (SVM) and Artificial Neural Network (ANN) models for the prediction of option price. Both models are tested with a benchmark publicly available dataset namely SPY option price-2015 in both testing and training phases. The converted data through Principal Component Analysis (PCA) is used in both models to achieve better prediction accuracy. On the other hand, the entire dataset is partitioned into two groups of training (70%) and test sets (30%) to avoid overfitting problem. The outcomes of the SVM model are compared with those of the ANN model based on the root mean square errors (RMSE). It is demonstrated by the experimental results that the ANN model performs better than the SVM model, and the predicted option prices are in good agreement with the corresponding actual option prices.展开更多
In this article, we consider the global chaotic synchronization of general cou- pled neural networks, in which subsystems have both discrete and distributed delays. Stochastic perturbations between subsystems are also...In this article, we consider the global chaotic synchronization of general cou- pled neural networks, in which subsystems have both discrete and distributed delays. Stochastic perturbations between subsystems are also considered. On the basis of two sim- ple adaptive pinning feedback control schemes, Lyapunov functional method, and stochas- tic analysis approach, several sufficient conditions are developed to guarantee global syn- chronization of the coupled neural networks with two kinds of delay couplings, even if only partial states of the nodes are coupled. The outer-coupling matrices may be symmetric or asymmetric. Unlike existing results that an isolate node is introduced as the pinning target, we pin to help the network realizing synchronization without introducing any iso- late node when the network is not synchronized. As a by product, sufficient conditions under which the network realizes synchronization without control are derived. Numerical simulations confirm the effectiveness of the obtained results.展开更多
A new intrusion detection method based on learning vector quantization （LVQ） with low overhead and high efficiency is presented. The computer vision system employs LVQ neural networks as classifier to recognize intr...A new intrusion detection method based on learning vector quantization （LVQ） with low overhead and high efficiency is presented. The computer vision system employs LVQ neural networks as classifier to recognize intrusion. The recognition process includes three stages： （1） feature selection and data normalization processing;（2） learning the training data selected from the feature data set; （3） identifying the intrusion and generating the result report of machine condition classification. Experimental results show that the proposed method is promising in terms of detection accuracy, computational expense and implementation for intrusion detection.展开更多
In this paper, sixty-eight research articles published between 2000 and 2017 as well as textbooks which employed four classification algorithms: K-Nearest-Neighbor (KNN), Support Vector Machines (SVM), Random Forest (...In this paper, sixty-eight research articles published between 2000 and 2017 as well as textbooks which employed four classification algorithms: K-Nearest-Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF) and Neural Network (NN) as the main statistical tools were reviewed. The aim was to examine and compare these nonparametric classification methods on the following attributes: robustness to training data, sensitivity to changes, data fitting, stability, ability to handle large data sizes, sensitivity to noise, time invested in parameter tuning, and accuracy. The performances, strengths and shortcomings of each of the algorithms were examined, and finally, a conclusion was arrived at on which one has higher performance. It was evident from the literature reviewed that RF is too sensitive to small changes in the training dataset and is occasionally unstable and tends to overfit in the model. KNN is easy to implement and understand but has a major drawback of becoming significantly slow as the size of the data in use grows, while the ideal value of K for the KNN classifier is difficult to set. SVM and RF are insensitive to noise or overtraining, which shows their ability in dealing with unbalanced data. Larger input datasets will lengthen classification times for NN and KNN more than for SVM and RF. Among these nonparametric classification methods, NN has the potential to become a more widely used classification algorithm, but because of their time-consuming parameter tuning procedure, high level of complexity in computational processing, the numerous types of NN architectures to choose from and the high number of algorithms used for training, most researchers recommend SVM and RF as easier and wieldy used methods which repeatedly achieve results with high accuracies and are often faster to implement.展开更多
In the contemporary era, the proliferation of information technology has led to an unprecedented surge in data generation, with this data being dispersed across a multitude of mobile devices. Facing these situations a...In the contemporary era, the proliferation of information technology has led to an unprecedented surge in data generation, with this data being dispersed across a multitude of mobile devices. Facing these situations and the training of deep learning model that needs great computing power support, the distributed algorithm that can carry out multi-party joint modeling has attracted everyone’s attention. The distributed training mode relieves the huge pressure of centralized model on computer computing power and communication. However, most distributed algorithms currently work in a master-slave mode, often including a central server for coordination, which to some extent will cause communication pressure, data leakage, privacy violations and other issues. To solve these problems, a decentralized fully distributed algorithm based on deep random weight neural network is proposed. The algorithm decomposes the original objective function into several sub-problems under consistency constraints, combines the decentralized average consensus (DAC) and alternating direction method of multipliers (ADMM), and achieves the goal of joint modeling and training through local calculation and communication of each node. Finally, we compare the proposed decentralized algorithm with several centralized deep neural networks with random weights, and experimental results demonstrate the effectiveness of the proposed algorithm.展开更多
In order to solve the problems of small sample over-fitting and local minima when neural networks learn online, a novel method of predicting network bandwidth based on support vector machines(SVM) is proposed. The pre...In order to solve the problems of small sample over-fitting and local minima when neural networks learn online, a novel method of predicting network bandwidth based on support vector machines(SVM) is proposed. The prediction and learning online will be completed by the proposed moving window learning algorithm(MWLA). The simulation research is done to validate the proposed method, which is compared with the method based on neural networks.展开更多
Machine learning method has been widely used in various geotechnical engineering risk analysis in recent years. However, the overfitting problem often occurs due to the small number of samples obtained in history. Thi...Machine learning method has been widely used in various geotechnical engineering risk analysis in recent years. However, the overfitting problem often occurs due to the small number of samples obtained in history. This paper proposes the FuzzySVM(support vector machine) geotechnical engineering risk analysis method based on the Bayesian network. The proposed method utilizes the fuzzy set theory to build a Bayesian network to reflect prior knowledge, and utilizes the SVM to build a Bayesian network to reflect historical samples. Then a Bayesian network for evaluation is built in Bayesian estimation method by combining prior knowledge with historical samples. Taking seismic damage evaluation of slopes as an example, the steps of the method are stated in detail. The proposed method is used to evaluate the seismic damage of 96 slopes along roads in the area affected by the Wenchuan earthquake. The evaluation results show that the method can solve the overfitting problem, which often occurs if the machine learning methods are used to evaluate risk of geotechnical engineering, and the performance of the method is much better than that of the previous machine learning methods. Moreover,the proposed method can also effectively evaluate various geotechnical engineering risks in the absence of some influencing factors.展开更多
故障根因分析旨在找到导致特定问题、故障或事件发生的原因,是多个领域中追踪溯源的重要支撑技术,但现有方法在效率、准确性和稳定性等方面仍不能满足故障根因分析任务的实际需求。对此,将贝叶斯网作为相关属性之间依赖关系表示和推理...故障根因分析旨在找到导致特定问题、故障或事件发生的原因,是多个领域中追踪溯源的重要支撑技术,但现有方法在效率、准确性和稳定性等方面仍不能满足故障根因分析任务的实际需求。对此,将贝叶斯网作为相关属性之间依赖关系表示和推理的知识框架,提出基于贝叶斯网的故障根因分析方法。首先,针对高维数据和稀疏样本带来的挑战,提出基于向量量化自编码器的高维属性约简算法,并给出α-BIC评分准则,高效地学习根因贝叶斯网(Root Cause Bayesian Network,RCBN)。随后,基于贝叶斯网嵌入技术实现RCBN的高效推理,高效计算各原因条件下故障产生的可能性,进而使用因果模型中的Blame机制度量各原因对给定故障的贡献度,从而实现故障根因分析。在3个公共数据集和3个合成数据集上的实验结果表明,所提方法的平均检测准确性和效率明显优于对比方法,在CHILD数据集上精度提升了7%,运行时间快了60%。展开更多
文摘A Vector Network Analyzer(VNA)can be used to identify oscillation frequency of a signal source with moderate or low Radio Frequency(RF)power if certain care is taken according to experimental results.Unlike reported in the literature that a resonant peak of measured absolute value of reflection coefficient greater than 1 that corresponds to an oscillation frequency,we report that by observing the magnitude change of one-port reflection coefficient across the entire swept frequency range,a sudden peak or a dip corresponds to an oscillation frequency,this is more accurate than other reports.In addition,using modern VNA as a signal detection method can significantly reduce measurement time and increase measurement accuracy to VNA capability for developing emerging signal generating devices at early stage,especially for planar,large quantity and operating in a wide frequency range.
基金financial support provided by the National Natural Science Foundation of China(Grant No.32172308)Startup Foundation for Doctors of Yan'an University(No.YDBK2022-79).
文摘As a simple,fast,and non-destructive measuring technology,dielectric spectroscopy is usually used to analyze the dielectric properties of agricultural products and food,and then to predict the main components of materials.However,the large and expensive vector network analyzers(VNA)with expensive analysis software applied in measuring dielectric properties make research limited to the laboratory.To acquire dielectric spectra in situ,a model for solving relative complex permittivity was derived,and its performance was validated.Then,a low-cost portable dielectric spectrometer with a mini VNA,a Raspberry Pi,and a coaxial probe as core parts was developed over the frequency range of 100-3000 MHz.The stability and accuracy of the developed spectrometer were tested using milk and juice.The results indicated that the relative errors of the model were within±5%for dielectric constant(ε′)and loss factor(ε″).The coefficients of variation of measuredε′andε″by the developed spectrometer at 100-3000 MHz were less than 1%and 2%,respectively.Compared with the dielectric properties obtained by using a commercial dielectric measurement system,the relative errors of measuredε′andε″were within±3.4%and±6.0%,respectively.This study makes fast,non-destructive,and on-site food quality detection using dielectric spectra possible.
基金Item Sponsored by National Natural Science Foundation of China(61290323,61333007,61473064)Fundamental Research Funds for Central Universities of China(N130108001)+1 种基金National High Technology Research and Development Program of China(2015AA043802)General Project on Scientific Research for Education Department of Liaoning Province of China(L20150186)
文摘Molten iron temperature as well as Si, P, and S contents is the most essential molten iron quality （MIQ） indices in the blast furnace （BF） ironmaking, which requires strict monitoring during the whole ironmaking production. However, these MIQ parameters are difficult to be directly measured online, and large-time delay exists in off-line analysis through laboratory sampling. Focusing on the practical challenge, a data-driven modeling method was presented for the prediction of MIQ using the improved muhivariable incremental random vector functional-link net- works （M-I-RVFLNs）. Compared with the conventional random vector functional-link networks （RVFLNs） and the online sequential RVFLNs, the M-I-RVFLNs have solved the problem of deciding the optimal number of hidden nodes and overcome the overfitting problems. Moreover, the proposed M I RVFLNs model has exhibited the potential for multivariable prediction of the MIQ and improved the terminal condition for the multiple-input multiple-out- put （MIMO） dynamic system, which is suitable for the BF ironmaking process in practice. Ultimately, industrial experiments and contrastive researches have been conducted on the BF No. 2 in Liuzhou Iron and Steel Group Co. Ltd. of China using the proposed method, and the results demonstrate that the established model produces better estima ting accuracy than other MIQ modeling methods.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61471377,61804181,61604177,and 61704191).
文摘Vector neural network(VNN)is one of the most important methods to process interval data.However,the VNN,which contains a great number of multiply-accumulate(MAC)operations,often adopts pure numerical calculation method,and thus is difficult to be miniaturized for the embedded applications.In this paper,we propose a memristor based vector-type backpropagation(MVTBP)architecture which utilizes memristive arrays to accelerate the MAC operations of interval data.Owing to the unique brain-like synaptic characteristics of memristive devices,e.g.,small size,low power consumption,and high integration density,the proposed architecture can be implemented with low area and power consumption cost and easily applied to embedded systems.The simulation results indicate that the proposed architecture has better identification performance and noise tolerance.When the device precision is 6 bits and the error deviation level(EDL)is 20%,the proposed architecture can achieve an identification rate,which is about 92%higher than that for interval-value testing sample and 81%higher than that for scalar-value testing sample.
文摘Accurate cost estimation at the early stage of a construction project is key factor in a project’s success. But it is difficult to quickly and accurately estimate construction costs at the planning stage, when drawings, documentation and the like are still incomplete. As such, various techniques have been applied to accurately estimate construction costs at an early stage, when project information is limited. While the various techniques have their pros and cons, there has been little effort made to determine the best technique in terms of cost estimating performance. The objective of this research is to compare the accuracy of three estimating techniques (regression analysis (RA), neural network (NN), and support vector machine techniques (SVM)) by performing estimations of construction costs. By comparing the accuracy of these techniques using historical cost data, it was found that NN model showed more accurate estimation results than the RA and SVM models. Consequently, it is determined that NN model is most suitable for estimating the cost of school building projects.
基金This work was supported partly by the National Natural Science Foundation of China (70501015, 70321001). The original version was presented at the Congress of the IFSR2005
文摘Option pricing has become one of the quite important parts of the financial market. As the market is always dynamic, it is really difficult to predict the option price accurately. For this reason, various machine learning techniques have been designed and developed to deal with the problem of predicting the future trend of option price. In this paper, we compare the effectiveness of Support Vector Machine (SVM) and Artificial Neural Network (ANN) models for the prediction of option price. Both models are tested with a benchmark publicly available dataset namely SPY option price-2015 in both testing and training phases. The converted data through Principal Component Analysis (PCA) is used in both models to achieve better prediction accuracy. On the other hand, the entire dataset is partitioned into two groups of training (70%) and test sets (30%) to avoid overfitting problem. The outcomes of the SVM model are compared with those of the ANN model based on the root mean square errors (RMSE). It is demonstrated by the experimental results that the ANN model performs better than the SVM model, and the predicted option prices are in good agreement with the corresponding actual option prices.
基金supported by the National Natural Science Foundation of China under Grant No. 60874088 and No. 11072059the Scientific Research Fund of Yunnan Province under Grant No. 2010ZC150the Scientific Research Fund of Yunnan Provincial Education Department under Grant No. 07Y10085
文摘In this article, we consider the global chaotic synchronization of general cou- pled neural networks, in which subsystems have both discrete and distributed delays. Stochastic perturbations between subsystems are also considered. On the basis of two sim- ple adaptive pinning feedback control schemes, Lyapunov functional method, and stochas- tic analysis approach, several sufficient conditions are developed to guarantee global syn- chronization of the coupled neural networks with two kinds of delay couplings, even if only partial states of the nodes are coupled. The outer-coupling matrices may be symmetric or asymmetric. Unlike existing results that an isolate node is introduced as the pinning target, we pin to help the network realizing synchronization without introducing any iso- late node when the network is not synchronized. As a by product, sufficient conditions under which the network realizes synchronization without control are derived. Numerical simulations confirm the effectiveness of the obtained results.
基金Supported by the National Natural Science Foundation of China (60573047), Natural Science Foundation of the Science and Technology Committee of Chongqing (8503) and the Applying Basic Research of the Education Committee of Chongqing (KJ060804)
文摘A new intrusion detection method based on learning vector quantization （LVQ） with low overhead and high efficiency is presented. The computer vision system employs LVQ neural networks as classifier to recognize intrusion. The recognition process includes three stages： （1） feature selection and data normalization processing;（2） learning the training data selected from the feature data set; （3） identifying the intrusion and generating the result report of machine condition classification. Experimental results show that the proposed method is promising in terms of detection accuracy, computational expense and implementation for intrusion detection.
文摘In this paper, sixty-eight research articles published between 2000 and 2017 as well as textbooks which employed four classification algorithms: K-Nearest-Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF) and Neural Network (NN) as the main statistical tools were reviewed. The aim was to examine and compare these nonparametric classification methods on the following attributes: robustness to training data, sensitivity to changes, data fitting, stability, ability to handle large data sizes, sensitivity to noise, time invested in parameter tuning, and accuracy. The performances, strengths and shortcomings of each of the algorithms were examined, and finally, a conclusion was arrived at on which one has higher performance. It was evident from the literature reviewed that RF is too sensitive to small changes in the training dataset and is occasionally unstable and tends to overfit in the model. KNN is easy to implement and understand but has a major drawback of becoming significantly slow as the size of the data in use grows, while the ideal value of K for the KNN classifier is difficult to set. SVM and RF are insensitive to noise or overtraining, which shows their ability in dealing with unbalanced data. Larger input datasets will lengthen classification times for NN and KNN more than for SVM and RF. Among these nonparametric classification methods, NN has the potential to become a more widely used classification algorithm, but because of their time-consuming parameter tuning procedure, high level of complexity in computational processing, the numerous types of NN architectures to choose from and the high number of algorithms used for training, most researchers recommend SVM and RF as easier and wieldy used methods which repeatedly achieve results with high accuracies and are often faster to implement.
文摘In the contemporary era, the proliferation of information technology has led to an unprecedented surge in data generation, with this data being dispersed across a multitude of mobile devices. Facing these situations and the training of deep learning model that needs great computing power support, the distributed algorithm that can carry out multi-party joint modeling has attracted everyone’s attention. The distributed training mode relieves the huge pressure of centralized model on computer computing power and communication. However, most distributed algorithms currently work in a master-slave mode, often including a central server for coordination, which to some extent will cause communication pressure, data leakage, privacy violations and other issues. To solve these problems, a decentralized fully distributed algorithm based on deep random weight neural network is proposed. The algorithm decomposes the original objective function into several sub-problems under consistency constraints, combines the decentralized average consensus (DAC) and alternating direction method of multipliers (ADMM), and achieves the goal of joint modeling and training through local calculation and communication of each node. Finally, we compare the proposed decentralized algorithm with several centralized deep neural networks with random weights, and experimental results demonstrate the effectiveness of the proposed algorithm.
文摘In order to solve the problems of small sample over-fitting and local minima when neural networks learn online, a novel method of predicting network bandwidth based on support vector machines(SVM) is proposed. The prediction and learning online will be completed by the proposed moving window learning algorithm(MWLA). The simulation research is done to validate the proposed method, which is compared with the method based on neural networks.
基金supported by the National Key Research and Development Program (Grant No. 2017YFC0504901)Sichuan Traffic Construction Science and Technology Project(Grant No. 2016B2–2)Doctoral Innovation Fund Program of Southwest Jiaotong University(Grant No. D-CX201804)
文摘Machine learning method has been widely used in various geotechnical engineering risk analysis in recent years. However, the overfitting problem often occurs due to the small number of samples obtained in history. This paper proposes the FuzzySVM(support vector machine) geotechnical engineering risk analysis method based on the Bayesian network. The proposed method utilizes the fuzzy set theory to build a Bayesian network to reflect prior knowledge, and utilizes the SVM to build a Bayesian network to reflect historical samples. Then a Bayesian network for evaluation is built in Bayesian estimation method by combining prior knowledge with historical samples. Taking seismic damage evaluation of slopes as an example, the steps of the method are stated in detail. The proposed method is used to evaluate the seismic damage of 96 slopes along roads in the area affected by the Wenchuan earthquake. The evaluation results show that the method can solve the overfitting problem, which often occurs if the machine learning methods are used to evaluate risk of geotechnical engineering, and the performance of the method is much better than that of the previous machine learning methods. Moreover,the proposed method can also effectively evaluate various geotechnical engineering risks in the absence of some influencing factors.
文摘故障根因分析旨在找到导致特定问题、故障或事件发生的原因,是多个领域中追踪溯源的重要支撑技术,但现有方法在效率、准确性和稳定性等方面仍不能满足故障根因分析任务的实际需求。对此,将贝叶斯网作为相关属性之间依赖关系表示和推理的知识框架,提出基于贝叶斯网的故障根因分析方法。首先,针对高维数据和稀疏样本带来的挑战,提出基于向量量化自编码器的高维属性约简算法,并给出α-BIC评分准则,高效地学习根因贝叶斯网(Root Cause Bayesian Network,RCBN)。随后,基于贝叶斯网嵌入技术实现RCBN的高效推理,高效计算各原因条件下故障产生的可能性,进而使用因果模型中的Blame机制度量各原因对给定故障的贡献度,从而实现故障根因分析。在3个公共数据集和3个合成数据集上的实验结果表明,所提方法的平均检测准确性和效率明显优于对比方法,在CHILD数据集上精度提升了7%,运行时间快了60%。
