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Robust least squares projection twin SVM and its sparse solution 被引量:1
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作者 ZHOU Shuisheng ZHANG Wenmeng +1 位作者 CHEN Li XU Mingliang 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2023年第4期827-838,共12页
Least squares projection twin support vector machine(LSPTSVM)has faster computing speed than classical least squares support vector machine(LSSVM).However,LSPTSVM is sensitive to outliers and its solution lacks sparsi... Least squares projection twin support vector machine(LSPTSVM)has faster computing speed than classical least squares support vector machine(LSSVM).However,LSPTSVM is sensitive to outliers and its solution lacks sparsity.Therefore,it is difficult for LSPTSVM to process large-scale datasets with outliers.In this paper,we propose a robust LSPTSVM model(called R-LSPTSVM)by applying truncated least squares loss function.The robustness of R-LSPTSVM is proved from a weighted perspective.Furthermore,we obtain the sparse solution of R-LSPTSVM by using the pivoting Cholesky factorization method in primal space.Finally,the sparse R-LSPTSVM algorithm(SR-LSPTSVM)is proposed.Experimental results show that SR-LSPTSVM is insensitive to outliers and can deal with large-scale datasets fastly. 展开更多
关键词 OUTLIERS robust least squares projection twin support vector machine(R-LSPTSVM) low-rank approximation sparse solution
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A Projection Method for Multiindices Decision Making
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作者 Wang Yingming (Department of Automation, Xiamen University, 361005, P. R. China) 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 1998年第3期1-7,共7页
This paper takes the comprehensive evaluation of industrial economic benefit as an example and proposes a new method called projection method for Inultiindices decision making,which is essentially a kind of simple add... This paper takes the comprehensive evaluation of industrial economic benefit as an example and proposes a new method called projection method for Inultiindices decision making,which is essentially a kind of simple additive Weighting method, but they differ from each other in meaning. 展开更多
关键词 Multiindices decision making Economic benefit evaluation vector projection
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Projective vector fields on Finsler manifolds
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作者 TIAN Huang-jia 《Applied Mathematics(A Journal of Chinese Universities)》 SCIE CSCD 2014年第2期217-229,共13页
In this paper, we give the equation that characterizes projective vector fields on a Finsler manifold by the local coordinate. Moreover, we obtain a feature of the projective fields on the compact Finsler manifold wit... In this paper, we give the equation that characterizes projective vector fields on a Finsler manifold by the local coordinate. Moreover, we obtain a feature of the projective fields on the compact Finsler manifold with non-positive flag curvature and the non-existence of projective vector fields on the compact Finsler manifold with negative flag curvature. Furthermore, we deduce some expectable, but non-trivial relationships between geometric vector fields such as projective, affine, conformal, homothetic and Killing vector fields on a Finsler manifold. 展开更多
关键词 Finsler manifold projective vector field conformal vector field.
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Anti-noise performance of the pulse coupled neural network applied in discrimination of neutron and gamma-ray 被引量:3
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作者 Hao-Ran Liu Zhuo Zuo +3 位作者 Peng Li Bing-Qi Liu Lan Chang Yu-Cheng Yan 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2022年第6期89-101,共13页
In this study,the anti-noise performance of a pulse-coupled neural network(PCNN)was investigated in the neutron and gamma-ray(n-γ)discrimination field.The experiments were conducted in two groups.In the first group,r... In this study,the anti-noise performance of a pulse-coupled neural network(PCNN)was investigated in the neutron and gamma-ray(n-γ)discrimination field.The experiments were conducted in two groups.In the first group,radiation pulse signals were pre-processed using a Fourier filter to reduce the original noise in the signals,whereas in the second group,the original noise was left untouched to simulate an extremely high-noise scenario.For each part,artificial Gaussian noise with different intensity levels was added to the signals prior to the discrimination process.In the aforementioned conditions,the performance of the PCNN was evaluated and compared with five other commonly used methods of n-γdiscrimination:(1)zero crossing,(2)charge comparison,(3)vector projection,(4)falling edge percentage slope,and(5)frequency gradient analysis.The experimental results showed that the PCNN method significantly outperforms other methods with outstanding FoM-value at all noise levels.Furthermore,the fluctuations in FoM-value of PCNN were significantly better than those obtained via other methods at most noise levels and only slightly worse than those obtained via the charge comparison and zerocrossing methods under extreme noise conditions.Additionally,the changing patterns and fluctuations of the FoMvalue were evaluated under different noise conditions.Hence,based on the results,the parameter selection strategy of the PCNN was presented.In conclusion,the PCNN method is suitable for use in high-noise application scenarios for n-γdiscrimination because of its stability and remarkable discrimination performance.It does not rely on strict parameter settings and can realize satisfactory performance over a wide parameter range. 展开更多
关键词 Pulse coupled neural network Zero crossing Frequency gradient analysis vector projection Charge comparison Neutron and gamma-ray discrimination Pulse shape discrimination
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A NOVEL DIRECTION OF ARRIVAL ESTIMATOR 被引量:1
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作者 ShaoChao LuGuangyue BaoZheng 《Journal of Electronics(China)》 2004年第2期97-103,共7页
A Direction Of Arrival(DOA) estimator based on the signal separation principle is introduced, and one of representative multidimensional estimators is established by introducing Matrix Operator projection signal steer... A Direction Of Arrival(DOA) estimator based on the signal separation principle is introduced, and one of representative multidimensional estimators is established by introducing Matrix Operator projection signal steering Vector Excision(MOVE) operation. Thanks to Alternating Separation (AS) technique, the multidimensional problem is transformed into a series of one-dimensional optimal ones. Furthermore, an equivalent simplified implementation of the AS is obtained. Finally the definiteness and uniqueness of the estimator are analyzed. 展开更多
关键词 Matrix Operator projection signal steering vector Excision(MOVE) estimator Alternating Separation(AS) algorithm Alternating projection algorithm
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A comparative survey of SSVEP recognition algorithms based on template matching of training trials
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作者 Tian-Jian Luo 《International Journal of Intelligent Computing and Cybernetics》 EI 2023年第1期46-67,共22页
Purpose-Steady-state visual evoked potential(SSVEP)has been widely used in the application of electroencephalogram(EEG)based non-invasive brain computer interface(BCI)due to its characteristics of high accuracy and in... Purpose-Steady-state visual evoked potential(SSVEP)has been widely used in the application of electroencephalogram(EEG)based non-invasive brain computer interface(BCI)due to its characteristics of high accuracy and information transfer rate(ITR).To recognize the SSVEP components in collected EEG trials,a lot of recognition algorithms based on template matching of training trials have been proposed and applied in recent years.In this paper,a comparative survey of SSVEP recognition algorithms based on template matching of training trails has been done.Design/methodology/approach-To survey and compare the recently proposed recognition algorithms for SSVEP,this paper regarded the conventional canonical correlated analysis(CCA)as the baseline,and selected individual template CCA(ITCCA),multi-set CCA(MsetCCA),task related component analysis(TRCA),latent common source extraction(LCSE)and a sum of squared correlation(SSCOR)for comparison.Findings-For the horizontal comparative of the six surveyed recognition algorithms,this paper adopted the“Tsinghua JFPM-SSVEP”data set and compared the average recognition performance on such data set.The comparative contents including:recognition accuracy,ITR,correlated coefficient and R-square values under different time duration of the SSVEP stimulus presentation.Based on the optimal time duration of stimulus presentation,the author has also compared the efficiency of the six compared algorithms.To measure the influence of different parameters,the number of training trials,the number of electrodes and the usage of filter bank preprocessing were compared in the ablation study.Originality/value-Based on the comparative results,this paper analyzed the advantages and disadvantages of the six compared SSVEP recognition algorithms by considering application scenes,realtime and computational complexity.Finally,the author gives the algorithms selection range for the recognition of real-world online SSVEP-BCI. 展开更多
关键词 Non-invasive brain-computer interface EEG signals Template matching of training trials Steadystate visual evoked potential Optimal projection vector
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Application of a new SPA-SVM coupling method for QSPR study of electrophoretic mobilities of some organic and inorganic compounds 被引量:1
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作者 Nasser Goudarzi Mohammad Goodarzi +1 位作者 M.Arab Chamjangali M.H.Fatemi 《Chinese Chemical Letters》 SCIE CAS CSCD 2013年第10期904-908,共5页
In this work, two chemometrics methods are applied for the modeling and prediction of electrophoretic mobilities of some organic and inorganic compounds. The successive projection algorithm, feature selection (SPA) ... In this work, two chemometrics methods are applied for the modeling and prediction of electrophoretic mobilities of some organic and inorganic compounds. The successive projection algorithm, feature selection (SPA) strategy, is used as the descriptor selection and model development method. Then, the support vector machine (SVM) and multiple linear regression (MLR) model are utilized to construct the non-linear and linear quantitative structure-property relationship models. The results obtained using the SVM model are compared with those obtained using MLR reveal that the SVM model is of much better predictive value than the MLR one. The root-mean-square errors for the training set and the test set for the SVM model were 0.1911 and 0.2569, respectively, while by the MLR model, they were 0.4908 and 0.6494, respectively. The results show that the SVM model drastically enhances the ability of prediction in QSPR studies and is superior to the MLR model. 展开更多
关键词 Quantitative structure-mobility relationship Support vector machine Electrophoretic mobility Successive projection algorithm Multiple linear regression
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