In this research,an integrated classification method based on principal component analysis-simulated annealing genetic algorithm-fuzzy cluster means(PCA-SAGA-FCM)was proposed for the unsupervised classification of tig...In this research,an integrated classification method based on principal component analysis-simulated annealing genetic algorithm-fuzzy cluster means(PCA-SAGA-FCM)was proposed for the unsupervised classification of tight sandstone reservoirs which lack the prior information and core experiments.A variety of evaluation parameters were selected,including lithology characteristic parameters,poro-permeability quality characteristic parameters,engineering quality characteristic parameters,and pore structure characteristic parameters.The PCA was used to reduce the dimension of the evaluation pa-rameters,and the low-dimensional data was used as input.The unsupervised reservoir classification of tight sandstone reservoir was carried out by the SAGA-FCM,the characteristics of reservoir at different categories were analyzed and compared with the lithological profiles.The analysis results of numerical simulation and actual logging data show that:1)compared with FCM algorithm,SAGA-FCM has stronger stability and higher accuracy;2)the proposed method can cluster the reservoir flexibly and effectively according to the degree of membership;3)the results of reservoir integrated classification match well with the lithologic profle,which demonstrates the reliability of the classification method.展开更多
With the increasing variety of application software of meteorological satellite ground system, how to provide reasonable hardware resources and improve the efficiency of software is paid more and more attention. In th...With the increasing variety of application software of meteorological satellite ground system, how to provide reasonable hardware resources and improve the efficiency of software is paid more and more attention. In this paper, a set of software classification method based on software operating characteristics is proposed. The method uses software run-time resource consumption to describe the software running characteristics. Firstly, principal component analysis (PCA) is used to reduce the dimension of software running feature data and to interpret software characteristic information. Then the modified K-means algorithm was used to classify the meteorological data processing software. Finally, it combined with the results of principal component analysis to explain the significance of various types of integrated software operating characteristics. And it is used as the basis for optimizing the allocation of software hardware resources and improving the efficiency of software operation.展开更多
An improved face recognition method is proposed based on principal component analysis （PCA） compounded with genetic algorithm （GA）, named as genetic based principal component analysis （GPCA）. Initially the eigen...An improved face recognition method is proposed based on principal component analysis （PCA） compounded with genetic algorithm （GA）, named as genetic based principal component analysis （GPCA）. Initially the eigenspace is created with eigenvalues and eigenvectors. From this space, the eigenfaces are constructed, and the most relevant eigenfaees have been selected using GPCA. With these eigenfaees, the input images are classified based on Euclidian distance. The proposed method was tested on ORL （Olivetti Research Labs） face database. Experimental results on this database demonstrate that the effectiveness of the proposed method for face recognition has less misclassification in comparison with previous methods.展开更多
Based on improved multi-objective particle swarm optimization（MOPSO） algorithm with principal component analysis（PCA） methodology, an efficient high-dimension multiobjective optimization method is proposed, which,...Based on improved multi-objective particle swarm optimization（MOPSO） algorithm with principal component analysis（PCA） methodology, an efficient high-dimension multiobjective optimization method is proposed, which, as the purpose of this paper, aims to improve the convergence of Pareto front in multi-objective optimization design. The mathematical efficiency,the physical reasonableness and the reliability in dealing with redundant objectives of PCA are verified by typical DTLZ5 test function and multi-objective correlation analysis of supercritical airfoil,and the proposed method is integrated into aircraft multi-disciplinary design（AMDEsign） platform, which contains aerodynamics, stealth and structure weight analysis and optimization module.Then the proposed method is used for the multi-point integrated aerodynamic optimization of a wide-body passenger aircraft, in which the redundant objectives identified by PCA are transformed to optimization constraints, and several design methods are compared. The design results illustrate that the strategy used in this paper is sufficient and multi-point design requirements of the passenger aircraft are reached. The visualization level of non-dominant Pareto set is improved by effectively reducing the dimension without losing the primary feature of the problem.展开更多
Support vector classifier(SVC)has the superior advantages for small sample learning problems with high dimensions,with especially better generalization ability.However there is some redundancy among the high dimension...Support vector classifier(SVC)has the superior advantages for small sample learning problems with high dimensions,with especially better generalization ability.However there is some redundancy among the high dimensions of the original samples and the main features of the samples may be picked up first to improve the performance of SVC.A principal component analysis(PCA)is employed to reduce the feature dimensions of the original samples and the pre-selected main features efficiently,and an SVC is constructed in the selected feature space to improve the learning speed and identification rate of SVC.Furthermore,a heuristic genetic algorithm-based automatic model selection is proposed to determine the hyperparameters of SVC to evaluate the performance of the learning machines.Experiments performed on the Heart and Adult benchmark data sets demonstrate that the proposed PCA-based SVC not only reduces the test time drastically,but also improves the identify rates effectively.展开更多
Liquid state methanol and ethanol under different temperatures have been investigated by FT-NIR(Fourier transform nearinfrared) spectroscopy,generalized two-dimensional(2D) correlation spectroscopy,and PCA(principal c...Liquid state methanol and ethanol under different temperatures have been investigated by FT-NIR(Fourier transform nearinfrared) spectroscopy,generalized two-dimensional(2D) correlation spectroscopy,and PCA(principal component analysis) . First,the FT-NIR spectra were measured over a temperature range of 30-64(or 30-71) °C,and then the 2D correlation spectra were computed.Combining near-infrared spectroscopy,generalized 2D correlation spectroscopy,and references,we analyzed the molecular structures(especially the hydrogen bond) of methanol and ethanol,and performed the NIR band assignments. The PCA method was employed to verify the results of the 2D analysis.This study will be helpful to the understanding of these reagents.展开更多
Kernal factor analysis （KFA） with vafimax was proposed by using Mercer kernel function which can map the data in the original space to a high-dimensional feature space, and was compared with the kernel principle com...Kernal factor analysis （KFA） with vafimax was proposed by using Mercer kernel function which can map the data in the original space to a high-dimensional feature space, and was compared with the kernel principle component analysis （KPCA）. The results show that the best error rate in handwritten digit recognition by kernel factor analysis with vadmax （4.2%） was superior to KPCA （4.4%）. The KFA with varimax could more accurately image handwritten digit recognition.展开更多
The convergence of algorithms used for principal component analysis is analyzed. The algorithms are proved to converge to eigenvectors and eigenvalues of a matrix A which is the expectation of observed random samples....The convergence of algorithms used for principal component analysis is analyzed. The algorithms are proved to converge to eigenvectors and eigenvalues of a matrix A which is the expectation of observed random samples. The conditions required here are considerably weaker than those used in previous work.展开更多
In order to predict the coal outburst risk quickly and accurately,a PCA-FA-SVM based coal and gas outburst risk prediction model was designed.Principal component analysis(PCA)was used to pre-process the original data ...In order to predict the coal outburst risk quickly and accurately,a PCA-FA-SVM based coal and gas outburst risk prediction model was designed.Principal component analysis(PCA)was used to pre-process the original data samples,extract the principal components of the samples,use firefly algorithm(FA)to improve the support vector machine model,and compare and analyze the prediction results of PCA-FA-SVM model with BP model,FA-SVM model,FA-BP model and SVM model.Accuracy rate,recall rate,Macro-F1 and model prediction time were used as evaluation indexes.The results show that:Principal component analysis improves the prediction efficiency and accuracy of FA-SVM model.The accuracy rate of PCA-FA-SVM model predicting coal and gas outburst risk is 0.962,recall rate is 0.955,Macro-F1 is 0.957,and model prediction time is 0.312s.Compared with other models,The comprehensive performance of PCA-FA-SVM model is better.展开更多
In this paper we propose an approach of prin-cipal component cluster analysis based on Lyapunov expo-nent spectrum (LES) to analyze the ECG time series. Analy-sis results of 22 sample-files of ECG from the MIT-BIH da-...In this paper we propose an approach of prin-cipal component cluster analysis based on Lyapunov expo-nent spectrum (LES) to analyze the ECG time series. Analy-sis results of 22 sample-files of ECG from the MIT-BIH da-tabase confirmed the validity of our approach. Another technique named improved teacher selecting student (TSS) algorithm is presented to analyze unknown samples by means of some known ones, which is of better accuracy. This technique combines the advantages of both statistical and nonlinear dynamical methods and is shown to be significant to the analysis of nonlinear ECG time series.展开更多
This paper puts forward a risk analysis model for software projects using enranced neural networks.The data for analysis are acquired through questionnaires from real software projects. To solve the multicollinearity ...This paper puts forward a risk analysis model for software projects using enranced neural networks.The data for analysis are acquired through questionnaires from real software projects. To solve the multicollinearity in software risks, the method of principal components analysis is adopted in the model to enhance network stability.To solve uncertainty of the neural networks structure and the uncertainty of the initial weights, genetic algorithms is employed.The experimental result reveals that the precision of software risk analysis can be improved by using the erhanced neural networks model.展开更多
In order to enhance the accuracy and efficiency of coal and gas outburst prediction,a novel approach combining Kernel Principal Component Analysis(KPCA)with an Improved Whale Optimization Algorithm(IWOA)optimized extr...In order to enhance the accuracy and efficiency of coal and gas outburst prediction,a novel approach combining Kernel Principal Component Analysis(KPCA)with an Improved Whale Optimization Algorithm(IWOA)optimized extreme learning machine(ELM)is proposed for precise forecasting of coal and gas outburst disasters in mines.Firstly,based on the influencing factors of coal and gas outburst disasters,nine coupling indexes are selected,including gas pressure,geological structure,initial velocity of gas emission,and coal structure type.The correlation between each index was analyzed using the Pearson correlation coefficient matrix in SPSS 27,followed by extraction of the principal components of the original data through Kernel Principal Component Analysis(KPCA).The Whale Optimization Algorithm(WOA)was enhanced by incorporating adaptive weight,variable helix position update,and optimal neighborhood disturbance to augment its performance.The improved Whale Optimization Algorithm(IWOA)is subsequently employed to optimize the weight Φ of the Extreme Learning Machine(ELM)input layer and the threshold g of the hidden layer,thereby enhancing its predictive accuracy and mitigating the issue of"over-fitting"associated with ELM to some extent.The principal components extracted by KPCA were utilized as input,while the outburst risk grade served as output.Subsequently,a comparative analysis was conducted between these results and those obtained from WOA-SVC,PSO-BPNN,and SSA-RF models.The IWOA-ELM model accurately predicts the risk grade of coal and gas outburst disasters,with results consistent with actual situations.Compared to other models tested,the model's performance showed an increase in Ac by 0.2,0.3,and 0.2 respectively;P increased by 0.15,0.2167,and 0.1333 respectively;R increased by 0.25,0.3,and 0.2333 respectively;F1-Score increased by 0.2031,0.2607,and 0.1864 respectively;Kappa coefficient k increased by 0.3226,0.4762 and 0.3175,respectively.The practicality and stability of the IWOAELM model were verified through its application in a coal mine in Shanxi Province where the predicted values exactly matched the actual values.This indicates that this model is more suitable for predicting coal and gas outburst disaster risks.展开更多
To make up the poor quality defects of traditional control methods and meet the growing requirements of accuracy for strip crown,an optimized model based on support vector machine(SVM)is put forward firstly to enhance...To make up the poor quality defects of traditional control methods and meet the growing requirements of accuracy for strip crown,an optimized model based on support vector machine(SVM)is put forward firstly to enhance the quality of product in hot strip rolling.Meanwhile,for enriching data information and ensuring data quality,experimental data were collected from a hot-rolled plant to set up prediction models,as well as the prediction performance of models was evaluated by calculating multiple indicators.Furthermore,the traditional SVM model and the combined prediction models with particle swarm optimization(PSO)algorithm and the principal component analysis combined with cuckoo search(PCA-CS)optimization strategies are presented to make a comparison.Besides,the prediction performance comparisons of the three models are discussed.Finally,the experimental results revealed that the PCA-CS-SVM model has the highest prediction accuracy and the fastest convergence speed.Furthermore,the root mean squared error(RMSE)of PCA-CS-SVM model is 2.04μm,and 98.15%of prediction data have an absolute error of less than 4.5μm.Especially,the results also proved that PCA-CS-SVM model not only satisfies precision requirement but also has certain guiding significance for the actual production of hot strip rolling.展开更多
When the electronic nose is used to identify different varieties of distilled liquors, the pattern recognition algorithm is chosen on the basis of the experience, which lacks the guiding principle. In this research, t...When the electronic nose is used to identify different varieties of distilled liquors, the pattern recognition algorithm is chosen on the basis of the experience, which lacks the guiding principle. In this research, the different brands of distilled spirits were identified using the pattern recognition algorithms （principal component analysis and the artificial neural network）. The recognition rates of different algorithms were compared. The recognition rate of the Back Propagation Neural Network （BPNN） is the highest. Owing to the slow convergence speed of the BPNN, it tends easily to get into a local minimum. A chaotic BPNN was tried in order to overcome the disadvantage of the BPNN. The convergence speed of the chaotic BPNN is 75.5 times faster than that of the BPNN.展开更多
The eigenface method that uses principal component analysis(PCA) has been the standard and popular method used in face recognition.This paper presents a PCA-memetic algorithm(PCA-MA) approach for feature selection.PCA...The eigenface method that uses principal component analysis(PCA) has been the standard and popular method used in face recognition.This paper presents a PCA-memetic algorithm(PCA-MA) approach for feature selection.PCA has been extended by MAs where the former was used for feature extraction/dimensionality reduction and the latter exploited for feature selection.Simulations were performed over ORL and YaleB face databases using Euclidean norm as the classifier.It was found that as far as the recognition rate is concerned,PCA-MA completely outperforms the eigenface method.We compared the performance of PCA extended with genetic algorithm(PCA-GA) with our proposed PCA-MA method.The results also clearly established the supremacy of the PCA-MA method over the PCA-GA method.We further extended linear discriminant analysis(LDA) and kernel principal component analysis(KPCA) approaches with the MA and observed significant improvement in recognition rate with fewer features.This paper also compares the performance of PCA-MA,LDA-MA and KPCA-MA approaches.展开更多
Multivariate statistical process monitoring methods are often used in chemical process fault diagnosis.In this article,(I)the cycle temporal algorithm(CTA)combined with the dynamic kernel principal component analysis(...Multivariate statistical process monitoring methods are often used in chemical process fault diagnosis.In this article,(I)the cycle temporal algorithm(CTA)combined with the dynamic kernel principal component analysis(DKPCA)and the multiway dynamic kernel principal component analysis(MDKPCA)fault detection algorithms are proposed,which are used for continuous and batch process fault detections,respectively.In addition,(II)a fault variable identification model based on reconstructed-based contribution(RBC)model that paves the way for determining the cause of the fault are proposed.The proposed fault diagnosis model was applied to Tennessee Eastman(TE)process and penicillin fermentation process for fault diagnosis.And compare with other fault diagnosis methods.The results show that the proposed method has better detection effects than other methods.Finally,the reconstruction-based contribution(RBC)model method is used to accurately locate the root cause of the fault and determine the fault path.展开更多
Principal component analysis(PCA) is fundamental in many pattern recognition applications.Much research has been performed to minimize the reconstruction error in L1-norm based reconstruction error minimization(L1-PCA...Principal component analysis(PCA) is fundamental in many pattern recognition applications.Much research has been performed to minimize the reconstruction error in L1-norm based reconstruction error minimization(L1-PCA-REM) since conventional L2-norm based PCA(L2-PCA) is sensitive to outliers.Recently,the variance maximization formulation of PCA with L1-norm(L1-PCA-VM) has been proposed,where new greedy and nongreedy solutions are developed.Armed with the gradient ascent perspective for optimization,we show that the L1-PCA-VM formulation is problematic in learning principal components and that only a greedy solution can achieve robustness motivation,which are verified by experiments on synthetic and real-world datasets.展开更多
Generative Adversarial Networks(GANs)are neural networks that allow models to learn deep representations without requiring a large amount of training data.Semi-Supervised GAN Classifiers are a recent innovation in GAN...Generative Adversarial Networks(GANs)are neural networks that allow models to learn deep representations without requiring a large amount of training data.Semi-Supervised GAN Classifiers are a recent innovation in GANs,where GANs are used to classify generated images into real and fake and multiple classes,similar to a general multi-class classifier.However,GANs have a sophisticated design that can be challenging to train.This is because obtaining the proper set of parameters for all models-generator,discriminator,and classifier is complex.As a result,training a single GAN model for different datasets may not produce satisfactory results.Therefore,this study proposes an SGAN model(Semi-Supervised GAN Classifier).First,a baseline model was constructed.The model was then enhanced by leveraging the Sine-Cosine Algorithm and Synthetic Minority Oversampling Technique(SMOTE).SMOTE was used to address class imbalances in the dataset,while Sine Cosine Algorithm(SCA)was used to optimize the weights of the classifier models.The optimal set of hyperparameters(learning rate and batch size)were obtained using grid manual search.Four well-known benchmark datasets and a set of evaluation measures were used to validate the proposed model.The proposed method was then compared against existing models,and the results on each dataset were recorded and demonstrated the effectiveness of the proposed model.The proposed model successfully showed improved test accuracy scores of 1%,2%,15%,and 5%on benchmarking multimedia datasets;Modified National Institute of Standards and Technology(MNIST)digits,Fashion MNIST,Pneumonia Chest X-ray,and Facial Emotion Detection Dataset,respectively.展开更多
This paper aims to provide an efficient and straightforward structural form-finding method for designers to extrapolate component forms during the conceptual stage.The core idea is to optimize the classical method of ...This paper aims to provide an efficient and straightforward structural form-finding method for designers to extrapolate component forms during the conceptual stage.The core idea is to optimize the classical method of structural form-finding based on principal stress lines by using parametric tools.The traditional operating process of this method relies excessively on the designer’s engineering experience and lacks precision.Meanwhile,the current optimization work for this method is overly complicated for architects,and limitations in component type and final result exist.Therefore,to facilitate an architect’s conceptual work,the optimization metrics of the method in this paper are set as simplicity,practicality,freedom,and rapid feedback.For that reason,this paper optimizes the method from three aspects:modeling strategy for continuum structures,classification processing of data by using the k-nearest neighbor algorithm,and topological form-finding process based on stress lines.Eventually,it allows architects to create structural texture with formal aesthetics and modify it in real time on the basis of structural analysis results.This paper also explores a comprehensive application strategy with internal force analysis diagramming to form-finding.The finite element analysis tool Karamba3D verifies the structural performance of the form-finding method.The performance is compared with that of the conventional form,and the comparison results show the practicality and potential of the strategy in this paper.展开更多
By introducing an arbitrary diagonal matrix, a generalized energy function (GEF) is proposed for searching for the optimum weights of a two layer linear neural network. From the GEF, we derive a recur- sive least squa...By introducing an arbitrary diagonal matrix, a generalized energy function (GEF) is proposed for searching for the optimum weights of a two layer linear neural network. From the GEF, we derive a recur- sive least squares (RLS) algorithm to extract in parallel multiple principal components of the input covari- ance matrix without designing an asymmetrical circuit. The local stability of the GEF algorithm at the equilibrium is analytically verified. Simulation results show that the GEF algorithm for parallel multiple principal components extraction exhibits the fast convergence and has the improved robustness resis- tance to the eigenvalue spread of the input covariance matrix as compared to the well-known lateral inhi- bition model (APEX) and least mean square error reconstruction (LMSER) algorithms.展开更多
基金funded by the National Natural Science Foundation of China(42174131)the Strategic Cooperation Technology Projects of CNPC and CUPB(ZLZX2020-03).
文摘In this research,an integrated classification method based on principal component analysis-simulated annealing genetic algorithm-fuzzy cluster means(PCA-SAGA-FCM)was proposed for the unsupervised classification of tight sandstone reservoirs which lack the prior information and core experiments.A variety of evaluation parameters were selected,including lithology characteristic parameters,poro-permeability quality characteristic parameters,engineering quality characteristic parameters,and pore structure characteristic parameters.The PCA was used to reduce the dimension of the evaluation pa-rameters,and the low-dimensional data was used as input.The unsupervised reservoir classification of tight sandstone reservoir was carried out by the SAGA-FCM,the characteristics of reservoir at different categories were analyzed and compared with the lithological profiles.The analysis results of numerical simulation and actual logging data show that:1)compared with FCM algorithm,SAGA-FCM has stronger stability and higher accuracy;2)the proposed method can cluster the reservoir flexibly and effectively according to the degree of membership;3)the results of reservoir integrated classification match well with the lithologic profle,which demonstrates the reliability of the classification method.
文摘With the increasing variety of application software of meteorological satellite ground system, how to provide reasonable hardware resources and improve the efficiency of software is paid more and more attention. In this paper, a set of software classification method based on software operating characteristics is proposed. The method uses software run-time resource consumption to describe the software running characteristics. Firstly, principal component analysis (PCA) is used to reduce the dimension of software running feature data and to interpret software characteristic information. Then the modified K-means algorithm was used to classify the meteorological data processing software. Finally, it combined with the results of principal component analysis to explain the significance of various types of integrated software operating characteristics. And it is used as the basis for optimizing the allocation of software hardware resources and improving the efficiency of software operation.
文摘An improved face recognition method is proposed based on principal component analysis （PCA） compounded with genetic algorithm （GA）, named as genetic based principal component analysis （GPCA）. Initially the eigenspace is created with eigenvalues and eigenvectors. From this space, the eigenfaces are constructed, and the most relevant eigenfaees have been selected using GPCA. With these eigenfaees, the input images are classified based on Euclidian distance. The proposed method was tested on ORL （Olivetti Research Labs） face database. Experimental results on this database demonstrate that the effectiveness of the proposed method for face recognition has less misclassification in comparison with previous methods.
基金supported by the National Natural Science Foundation of China (No.11402288)
文摘Based on improved multi-objective particle swarm optimization（MOPSO） algorithm with principal component analysis（PCA） methodology, an efficient high-dimension multiobjective optimization method is proposed, which, as the purpose of this paper, aims to improve the convergence of Pareto front in multi-objective optimization design. The mathematical efficiency,the physical reasonableness and the reliability in dealing with redundant objectives of PCA are verified by typical DTLZ5 test function and multi-objective correlation analysis of supercritical airfoil,and the proposed method is integrated into aircraft multi-disciplinary design（AMDEsign） platform, which contains aerodynamics, stealth and structure weight analysis and optimization module.Then the proposed method is used for the multi-point integrated aerodynamic optimization of a wide-body passenger aircraft, in which the redundant objectives identified by PCA are transformed to optimization constraints, and several design methods are compared. The design results illustrate that the strategy used in this paper is sufficient and multi-point design requirements of the passenger aircraft are reached. The visualization level of non-dominant Pareto set is improved by effectively reducing the dimension without losing the primary feature of the problem.
基金the National Natural Science of China(50675167)a Foundation for the Author of National Excellent Doctoral Dissertation of China(200535)
文摘Support vector classifier(SVC)has the superior advantages for small sample learning problems with high dimensions,with especially better generalization ability.However there is some redundancy among the high dimensions of the original samples and the main features of the samples may be picked up first to improve the performance of SVC.A principal component analysis(PCA)is employed to reduce the feature dimensions of the original samples and the pre-selected main features efficiently,and an SVC is constructed in the selected feature space to improve the learning speed and identification rate of SVC.Furthermore,a heuristic genetic algorithm-based automatic model selection is proposed to determine the hyperparameters of SVC to evaluate the performance of the learning machines.Experiments performed on the Heart and Adult benchmark data sets demonstrate that the proposed PCA-based SVC not only reduces the test time drastically,but also improves the identify rates effectively.
基金supported by the Medical Scientific Research Foundation of Guangdong Province,China(B2009043)
文摘Liquid state methanol and ethanol under different temperatures have been investigated by FT-NIR(Fourier transform nearinfrared) spectroscopy,generalized two-dimensional(2D) correlation spectroscopy,and PCA(principal component analysis) . First,the FT-NIR spectra were measured over a temperature range of 30-64(or 30-71) °C,and then the 2D correlation spectra were computed.Combining near-infrared spectroscopy,generalized 2D correlation spectroscopy,and references,we analyzed the molecular structures(especially the hydrogen bond) of methanol and ethanol,and performed the NIR band assignments. The PCA method was employed to verify the results of the 2D analysis.This study will be helpful to the understanding of these reagents.
基金The National Defence Foundation of China (No.NEWL51435Qt220401)
文摘Kernal factor analysis （KFA） with vafimax was proposed by using Mercer kernel function which can map the data in the original space to a high-dimensional feature space, and was compared with the kernel principle component analysis （KPCA）. The results show that the best error rate in handwritten digit recognition by kernel factor analysis with vadmax （4.2%） was superior to KPCA （4.4%）. The KFA with varimax could more accurately image handwritten digit recognition.
基金Project supported by the National Natural Science Foundation of China.
文摘The convergence of algorithms used for principal component analysis is analyzed. The algorithms are proved to converge to eigenvectors and eigenvalues of a matrix A which is the expectation of observed random samples. The conditions required here are considerably weaker than those used in previous work.
基金financially supported by the National Natural Science Foundation of China(52174117,52004117)Postdoctoral Science Foundation of China(2021T140290,2020M680975)Science and Technology Research Project of Liaoning Provincial Department of Education(LJ2020JCL005).
文摘In order to predict the coal outburst risk quickly and accurately,a PCA-FA-SVM based coal and gas outburst risk prediction model was designed.Principal component analysis(PCA)was used to pre-process the original data samples,extract the principal components of the samples,use firefly algorithm(FA)to improve the support vector machine model,and compare and analyze the prediction results of PCA-FA-SVM model with BP model,FA-SVM model,FA-BP model and SVM model.Accuracy rate,recall rate,Macro-F1 and model prediction time were used as evaluation indexes.The results show that:Principal component analysis improves the prediction efficiency and accuracy of FA-SVM model.The accuracy rate of PCA-FA-SVM model predicting coal and gas outburst risk is 0.962,recall rate is 0.955,Macro-F1 is 0.957,and model prediction time is 0.312s.Compared with other models,The comprehensive performance of PCA-FA-SVM model is better.
文摘In this paper we propose an approach of prin-cipal component cluster analysis based on Lyapunov expo-nent spectrum (LES) to analyze the ECG time series. Analy-sis results of 22 sample-files of ECG from the MIT-BIH da-tabase confirmed the validity of our approach. Another technique named improved teacher selecting student (TSS) algorithm is presented to analyze unknown samples by means of some known ones, which is of better accuracy. This technique combines the advantages of both statistical and nonlinear dynamical methods and is shown to be significant to the analysis of nonlinear ECG time series.
文摘This paper puts forward a risk analysis model for software projects using enranced neural networks.The data for analysis are acquired through questionnaires from real software projects. To solve the multicollinearity in software risks, the method of principal components analysis is adopted in the model to enhance network stability.To solve uncertainty of the neural networks structure and the uncertainty of the initial weights, genetic algorithms is employed.The experimental result reveals that the precision of software risk analysis can be improved by using the erhanced neural networks model.
基金supported by the National Natural Science Foun-dation of China (No.52174188)the Graduate Education Innovation Project of Shanxi Datong University of China (No.23CX49).
文摘In order to enhance the accuracy and efficiency of coal and gas outburst prediction,a novel approach combining Kernel Principal Component Analysis(KPCA)with an Improved Whale Optimization Algorithm(IWOA)optimized extreme learning machine(ELM)is proposed for precise forecasting of coal and gas outburst disasters in mines.Firstly,based on the influencing factors of coal and gas outburst disasters,nine coupling indexes are selected,including gas pressure,geological structure,initial velocity of gas emission,and coal structure type.The correlation between each index was analyzed using the Pearson correlation coefficient matrix in SPSS 27,followed by extraction of the principal components of the original data through Kernel Principal Component Analysis(KPCA).The Whale Optimization Algorithm(WOA)was enhanced by incorporating adaptive weight,variable helix position update,and optimal neighborhood disturbance to augment its performance.The improved Whale Optimization Algorithm(IWOA)is subsequently employed to optimize the weight Φ of the Extreme Learning Machine(ELM)input layer and the threshold g of the hidden layer,thereby enhancing its predictive accuracy and mitigating the issue of"over-fitting"associated with ELM to some extent.The principal components extracted by KPCA were utilized as input,while the outburst risk grade served as output.Subsequently,a comparative analysis was conducted between these results and those obtained from WOA-SVC,PSO-BPNN,and SSA-RF models.The IWOA-ELM model accurately predicts the risk grade of coal and gas outburst disasters,with results consistent with actual situations.Compared to other models tested,the model's performance showed an increase in Ac by 0.2,0.3,and 0.2 respectively;P increased by 0.15,0.2167,and 0.1333 respectively;R increased by 0.25,0.3,and 0.2333 respectively;F1-Score increased by 0.2031,0.2607,and 0.1864 respectively;Kappa coefficient k increased by 0.3226,0.4762 and 0.3175,respectively.The practicality and stability of the IWOAELM model were verified through its application in a coal mine in Shanxi Province where the predicted values exactly matched the actual values.This indicates that this model is more suitable for predicting coal and gas outburst disaster risks.
基金Project(52005358)supported by the National Natural Science Foundation of ChinaProject(2018YFB1307902)supported by the National Key R&D Program of China+1 种基金Project(201901D111243)supported by the Natural Science Foundation of Shanxi Province,ChinaProject(2019-KF-25-05)supported by the Natural Science Foundation of Liaoning Province,China。
文摘To make up the poor quality defects of traditional control methods and meet the growing requirements of accuracy for strip crown,an optimized model based on support vector machine(SVM)is put forward firstly to enhance the quality of product in hot strip rolling.Meanwhile,for enriching data information and ensuring data quality,experimental data were collected from a hot-rolled plant to set up prediction models,as well as the prediction performance of models was evaluated by calculating multiple indicators.Furthermore,the traditional SVM model and the combined prediction models with particle swarm optimization(PSO)algorithm and the principal component analysis combined with cuckoo search(PCA-CS)optimization strategies are presented to make a comparison.Besides,the prediction performance comparisons of the three models are discussed.Finally,the experimental results revealed that the PCA-CS-SVM model has the highest prediction accuracy and the fastest convergence speed.Furthermore,the root mean squared error(RMSE)of PCA-CS-SVM model is 2.04μm,and 98.15%of prediction data have an absolute error of less than 4.5μm.Especially,the results also proved that PCA-CS-SVM model not only satisfies precision requirement but also has certain guiding significance for the actual production of hot strip rolling.
基金the Science and Technology Plan Projects, Department of Education of Jilin Province, P R China (Grant no. 2006026)
文摘When the electronic nose is used to identify different varieties of distilled liquors, the pattern recognition algorithm is chosen on the basis of the experience, which lacks the guiding principle. In this research, the different brands of distilled spirits were identified using the pattern recognition algorithms （principal component analysis and the artificial neural network）. The recognition rates of different algorithms were compared. The recognition rate of the Back Propagation Neural Network （BPNN） is the highest. Owing to the slow convergence speed of the BPNN, it tends easily to get into a local minimum. A chaotic BPNN was tried in order to overcome the disadvantage of the BPNN. The convergence speed of the chaotic BPNN is 75.5 times faster than that of the BPNN.
文摘The eigenface method that uses principal component analysis(PCA) has been the standard and popular method used in face recognition.This paper presents a PCA-memetic algorithm(PCA-MA) approach for feature selection.PCA has been extended by MAs where the former was used for feature extraction/dimensionality reduction and the latter exploited for feature selection.Simulations were performed over ORL and YaleB face databases using Euclidean norm as the classifier.It was found that as far as the recognition rate is concerned,PCA-MA completely outperforms the eigenface method.We compared the performance of PCA extended with genetic algorithm(PCA-GA) with our proposed PCA-MA method.The results also clearly established the supremacy of the PCA-MA method over the PCA-GA method.We further extended linear discriminant analysis(LDA) and kernel principal component analysis(KPCA) approaches with the MA and observed significant improvement in recognition rate with fewer features.This paper also compares the performance of PCA-MA,LDA-MA and KPCA-MA approaches.
基金financial support from the National Natural Science Foundation of China (21706220)
文摘Multivariate statistical process monitoring methods are often used in chemical process fault diagnosis.In this article,(I)the cycle temporal algorithm(CTA)combined with the dynamic kernel principal component analysis(DKPCA)and the multiway dynamic kernel principal component analysis(MDKPCA)fault detection algorithms are proposed,which are used for continuous and batch process fault detections,respectively.In addition,(II)a fault variable identification model based on reconstructed-based contribution(RBC)model that paves the way for determining the cause of the fault are proposed.The proposed fault diagnosis model was applied to Tennessee Eastman(TE)process and penicillin fermentation process for fault diagnosis.And compare with other fault diagnosis methods.The results show that the proposed method has better detection effects than other methods.Finally,the reconstruction-based contribution(RBC)model method is used to accurately locate the root cause of the fault and determine the fault path.
基金Project supported by the National Natural Science Foundation of China (Nos. 61071131 and 61271388)the Beijing Natural Science Foundation (No. 4122040)+1 种基金the Research Project of Tsinghua University (No. 2012Z01011)the United Technologies Research Center (UTRC)
文摘Principal component analysis(PCA) is fundamental in many pattern recognition applications.Much research has been performed to minimize the reconstruction error in L1-norm based reconstruction error minimization(L1-PCA-REM) since conventional L2-norm based PCA(L2-PCA) is sensitive to outliers.Recently,the variance maximization formulation of PCA with L1-norm(L1-PCA-VM) has been proposed,where new greedy and nongreedy solutions are developed.Armed with the gradient ascent perspective for optimization,we show that the L1-PCA-VM formulation is problematic in learning principal components and that only a greedy solution can achieve robustness motivation,which are verified by experiments on synthetic and real-world datasets.
基金This research was supported by Universiti Teknologi PETRONAS,under the Yayasan Universiti Teknologi PETRONAS(YUTP)Fundamental Research Grant Scheme(YUTPFRG/015LC0-308).
文摘Generative Adversarial Networks(GANs)are neural networks that allow models to learn deep representations without requiring a large amount of training data.Semi-Supervised GAN Classifiers are a recent innovation in GANs,where GANs are used to classify generated images into real and fake and multiple classes,similar to a general multi-class classifier.However,GANs have a sophisticated design that can be challenging to train.This is because obtaining the proper set of parameters for all models-generator,discriminator,and classifier is complex.As a result,training a single GAN model for different datasets may not produce satisfactory results.Therefore,this study proposes an SGAN model(Semi-Supervised GAN Classifier).First,a baseline model was constructed.The model was then enhanced by leveraging the Sine-Cosine Algorithm and Synthetic Minority Oversampling Technique(SMOTE).SMOTE was used to address class imbalances in the dataset,while Sine Cosine Algorithm(SCA)was used to optimize the weights of the classifier models.The optimal set of hyperparameters(learning rate and batch size)were obtained using grid manual search.Four well-known benchmark datasets and a set of evaluation measures were used to validate the proposed model.The proposed method was then compared against existing models,and the results on each dataset were recorded and demonstrated the effectiveness of the proposed model.The proposed model successfully showed improved test accuracy scores of 1%,2%,15%,and 5%on benchmarking multimedia datasets;Modified National Institute of Standards and Technology(MNIST)digits,Fashion MNIST,Pneumonia Chest X-ray,and Facial Emotion Detection Dataset,respectively.
文摘This paper aims to provide an efficient and straightforward structural form-finding method for designers to extrapolate component forms during the conceptual stage.The core idea is to optimize the classical method of structural form-finding based on principal stress lines by using parametric tools.The traditional operating process of this method relies excessively on the designer’s engineering experience and lacks precision.Meanwhile,the current optimization work for this method is overly complicated for architects,and limitations in component type and final result exist.Therefore,to facilitate an architect’s conceptual work,the optimization metrics of the method in this paper are set as simplicity,practicality,freedom,and rapid feedback.For that reason,this paper optimizes the method from three aspects:modeling strategy for continuum structures,classification processing of data by using the k-nearest neighbor algorithm,and topological form-finding process based on stress lines.Eventually,it allows architects to create structural texture with formal aesthetics and modify it in real time on the basis of structural analysis results.This paper also explores a comprehensive application strategy with internal force analysis diagramming to form-finding.The finite element analysis tool Karamba3D verifies the structural performance of the form-finding method.The performance is compared with that of the conventional form,and the comparison results show the practicality and potential of the strategy in this paper.
基金supported in part by the National Natural Science Foundation of China(Grant Nos.60172011 and 69831040)Guangxi Natural Science Foundation(Grant No.gzk0007011)the Science Foundation of Guangxi Education Bureau,China
文摘By introducing an arbitrary diagonal matrix, a generalized energy function (GEF) is proposed for searching for the optimum weights of a two layer linear neural network. From the GEF, we derive a recur- sive least squares (RLS) algorithm to extract in parallel multiple principal components of the input covari- ance matrix without designing an asymmetrical circuit. The local stability of the GEF algorithm at the equilibrium is analytically verified. Simulation results show that the GEF algorithm for parallel multiple principal components extraction exhibits the fast convergence and has the improved robustness resis- tance to the eigenvalue spread of the input covariance matrix as compared to the well-known lateral inhi- bition model (APEX) and least mean square error reconstruction (LMSER) algorithms.
