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Constrained voting extreme learning machine and its application 被引量:5
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作者 MIN Mengcan CHEN Xiaofang XIE Yongfang 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2021年第1期209-219,共11页
Extreme learning machine(ELM)has been proved to be an effective pattern classification and regression learning mechanism by researchers.However,its good performance is based on a large number of hidden layer nodes.Wit... Extreme learning machine(ELM)has been proved to be an effective pattern classification and regression learning mechanism by researchers.However,its good performance is based on a large number of hidden layer nodes.With the increase of the nodes in the hidden layers,the computation cost is greatly increased.In this paper,we propose a novel algorithm,named constrained voting extreme learning machine(CV-ELM).Compared with the traditional ELM,the CV-ELM determines the input weight and bias based on the differences of between-class samples.At the same time,to improve the accuracy of the proposed method,the voting selection is introduced.The proposed method is evaluated on public benchmark datasets.The experimental results show that the proposed algorithm is superior to the original ELM algorithm.Further,we apply the CV-ELM to the classification of superheat degree(SD)state in the aluminum electrolysis industry,and the recognition accuracy rate reaches87.4%,and the experimental results demonstrate that the proposed method is more robust than the existing state-of-the-art identification methods. 展开更多
关键词 extreme learning machine(ELM) majority voting ensemble method sample based learning superheat degree(SD)
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Design and Research on Identification of Typical Tea Plant Diseases Using Small Sample Learning
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作者 Jian Yang 《Journal of Electronic Research and Application》 2024年第5期21-25,共5页
Tea plants are susceptible to diseases during their growth.These diseases seriously affect the yield and quality of tea.The effective prevention and control of diseases requires accurate identification of diseases.Wit... Tea plants are susceptible to diseases during their growth.These diseases seriously affect the yield and quality of tea.The effective prevention and control of diseases requires accurate identification of diseases.With the development of artificial intelligence and computer vision,automatic recognition of plant diseases using image features has become feasible.As the support vector machine(SVM)is suitable for high dimension,high noise,and small sample learning,this paper uses the support vector machine learning method to realize the segmentation of disease spots of diseased tea plants.An improved Conditional Deep Convolutional Generation Adversarial Network with Gradient Penalty(C-DCGAN-GP)was used to expand the segmentation of tea plant spots.Finally,the Visual Geometry Group 16(VGG16)deep learning classification network was trained by the expanded tea lesion images to realize tea disease recognition. 展开更多
关键词 Small sample learning Tea plant disease VGG16 deep learning
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How to Be a Critically Reflective Teacher in Language Teaching and Learning
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作者 Shuhua Yang 《Sino-US English Teaching》 2005年第5期31-35,共5页
The article holds that a critically reflective teacher has an important role to play in language teaching and learning process. Several samples of critically reflective teachers are introduced in the article to show t... The article holds that a critically reflective teacher has an important role to play in language teaching and learning process. Several samples of critically reflective teachers are introduced in the article to show that critically reflective teachers should be enthusiastic, creative and informative in language teaching and learning. 展开更多
关键词 critically reflective teachers samples reflections language teaching and learning
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Meshfree-based physics-informed neural networks for the unsteady Oseen equations
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作者 彭珂依 岳靖 +1 位作者 张文 李剑 《Chinese Physics B》 SCIE EI CAS CSCD 2023年第4期151-159,共9页
We propose the meshfree-based physics-informed neural networks for solving the unsteady Oseen equations.Firstly,based on the ideas of meshfree and small sample learning,we only randomly select a small number of spatio... We propose the meshfree-based physics-informed neural networks for solving the unsteady Oseen equations.Firstly,based on the ideas of meshfree and small sample learning,we only randomly select a small number of spatiotemporal points to train the neural network instead of forming a mesh.Specifically,we optimize the neural network by minimizing the loss function to satisfy the differential operators,initial condition and boundary condition.Then,we prove the convergence of the loss function and the convergence of the neural network.In addition,the feasibility and effectiveness of the method are verified by the results of numerical experiments,and the theoretical derivation is verified by the relative error between the neural network solution and the analytical solution. 展开更多
关键词 physics-informed neural networks the unsteady Oseen equation convergence small sample learning
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Bayesian discriminant analysis for prediction of coal and gas outbursts and application 被引量:10
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作者 WANG Chao WANG Enyuan XU Jiankun LIU Xiaofei LING Li 《Mining Science and Technology》 EI CAS 2010年第4期520-523,541,共5页
Based on the principle of Bayesian discriminant analysis, we established a model of Bayesian discriminant analysis for predicting coal and gas outbursts. We selected five major indices which affect outbursts, i.e., in... Based on the principle of Bayesian discriminant analysis, we established a model of Bayesian discriminant analysis for predicting coal and gas outbursts. We selected five major indices which affect outbursts, i.e., initial speed of methane diffusion, a consistent coal coefficient, gas pressure, destructive style of coal and mining depth, as discriminating factors of the model. In our model, we divided the type of coal and gas outbursts into four grades regarded as four normal populations. We then obtained the corresponding discriminant functions through training a set of data from engineering examples as learning samples and evaluated their criteria by a back substitution method to verify the optimal properties of the model. Finally, we applied the model to the prediction of coal and gas outbursts in the Yunnan Enhong Mine. Our results coincided completely with the actual situation. These results show that a model of Bayesian discriminant analysis has excellent recognition performance, high prediction accuracy and a low error rate and is an effective method to predict coal and gas outbursts. 展开更多
关键词 Bayesian discriminant analysis coal and gas outbursts learning samples PREDICTION
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Novel technique for cost reduction in mutation testing 被引量:1
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作者 蒋玉婷 李必信 《Journal of Southeast University(English Edition)》 EI CAS 2011年第1期17-21,共5页
Aimed at the problem of expensive costs in mutation testing which has hampered its wide use,a technique of introducing a test case selection into the process of mutation testing is proposed.For each mutant,a fixed num... Aimed at the problem of expensive costs in mutation testing which has hampered its wide use,a technique of introducing a test case selection into the process of mutation testing is proposed.For each mutant,a fixed number of test cases are selected to constrain the maximum allowable executions so as to reduce useless work.Test case selection largely depends on the degree of mutation.The mutation distance is an index describing the semantic difference between the original program and the mutated program.It represents the percentage of effective test cases in a test set,so it can be used to guide the selection of test cases.The bigger the mutation distance is,the easier it is that the mutant will be killed,so the corresponding number of effective test cases for this mutant is greater.Experimental results suggest that the technique can remarkably reduce execution costs without a significant loss of test effectiveness. 展开更多
关键词 mutation testing mutation distance sample learning execution cost test case selection
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A fault diagnosis model based on weighted extension neural network for turbo-generator sets on small samples with noise 被引量:11
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作者 Tichun WANG Jiayun WANG +1 位作者 Yong WU Xin SHENG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2020年第10期2757-2769,共13页
In data-driven fault diagnosis for turbo-generator sets,the fault samples are usually expensive to obtain,and inevitably with noise,which will both lead to an unsatisfying identification performance of diagnosis model... In data-driven fault diagnosis for turbo-generator sets,the fault samples are usually expensive to obtain,and inevitably with noise,which will both lead to an unsatisfying identification performance of diagnosis models.To address these issues,this paper proposes a fault diagnosis model for turbo-generator sets based on Weighted Extension Neural Network(W-ENN).WENN is a novel neural network which has three types of connection weights and an improved correlation function.The performance of the proposed model is validated against Extension Neural Network(ENN),Support Vector Machine(SVM),Relevance Vector Machine(RVM)and Extreme Learning Machine(ELM)based models.The results indicate that,on noisy small sample sets,the proposed model is superior to the other models in terms of higher identification accuracy with fewer samples and strong noise-tolerant ability.The findings of this study may serve as a powerful fault diagnosis model for turbo-generator sets on noisy small sample sets. 展开更多
关键词 Fault diagnosis samples with noise Small samples learning Turbo-generator sets Weighted Extension Neural Network
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The convergence rates of Shannon sampling learning algorithms 被引量:2
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作者 SHENG BaoHuai 《Science China Mathematics》 SCIE 2012年第6期1243-1256,共14页
In the present paper,we provide an error bound for the learning rates of the regularized Shannon sampling learning scheme when the hypothesis space is a reproducing kernel Hilbert space(RKHS) derived by a Mercer kerne... In the present paper,we provide an error bound for the learning rates of the regularized Shannon sampling learning scheme when the hypothesis space is a reproducing kernel Hilbert space(RKHS) derived by a Mercer kernel and a determined net.We show that if the sample is taken according to the determined set,then,the sample error can be bounded by the Mercer matrix with respect to the samples and the determined net.The regularization error may be bounded by the approximation order of the reproducing kernel Hilbert space interpolation operator.The paper is an investigation on a remark provided by Smale and Zhou. 展开更多
关键词 function reconstruction reproducing kernel Hilbert spaces Shannon sampling learning algorithm learning theory sample error regularization error
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Application of quasi-oppositional symbiotic organisms search based extreme learning machine for stock market prediction
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作者 Smita Rath Binod Kumar Sahu Manoj Ranjan Nayak 《International Journal of Intelligent Computing and Cybernetics》 EI 2019年第2期175-193,共19页
Purpose–Forecasting of stock indices is a challenging issue because stock data are dynamic,non-linear and uncertain in nature.Selection of an accurate forecasting model is very much essential to predict the next-day ... Purpose–Forecasting of stock indices is a challenging issue because stock data are dynamic,non-linear and uncertain in nature.Selection of an accurate forecasting model is very much essential to predict the next-day closing prices of the stock indices.The purpose of this paper is to develop an efficient and accurate forecasting model to predict the next-day closing prices of seven stock indices.Design/methodology/approach–A novel strategy called quasi-oppositional symbiotic organisms search-based extreme learning machine(QSOS-ELM)is proposed to forecast the next-day closing prices effectively.Accuracy in the prediction of closing price depends on output weights which are dependent on input weights and biases.This paper mainly deals with the optimal design of input weights and biases of the ELM prediction model using QSOS and SOS optimization algorithms.Findings–Simulation is carried out on seven stock indices,and performance analysis of QSOS-ELM and SOS-ELM prediction models is done by taking various statistical measures such as mean square error,mean absolute percentage error,accuracy and paired sample t-test.Comparative performance analysis reveals that the QSOS-ELM model outperforms the SOS-ELM model in predicting the next-day closing prices more accurately for all the seven stock indices under study.Originality/value–The QSOS-ELM prediction model and SOS-ELM are developed for the first time to predict the next-day closing prices of various stock indices.The paired t-test is also carried out for the first time in literature to hypothetically prove that there is a zero mean difference between the predicted and actual closing prices. 展开更多
关键词 Extreme learning machine Symbiotic organisms search Quasi-oppositional-based learning Paired sample t-test
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A robust and efficient structural reliability method combining radial-based importance sampling and Kriging 被引量:5
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作者 XIONG Bo TAN HuiFeng 《Science China(Technological Sciences)》 SCIE EI CAS CSCD 2018年第5期724-734,共11页
Simulation based structural reliability analysis suffers from a heavy computational burden, as each sample needs to be evaluated on the performance function, where structural analysis is performed. To alleviate the co... Simulation based structural reliability analysis suffers from a heavy computational burden, as each sample needs to be evaluated on the performance function, where structural analysis is performed. To alleviate the computational burden, related research focuses mainly on reduction of samples and application of surrogate model, which substitutes the performance function. However,the reduction of samples is achieved commonly at the expense of loss of robustness, and the construction of surrogate model is computationally expensive. In view of this, this paper presents a robust and efficient method in the same direction. The present method uses radial-based importance sampling (RBIS) to reduce samples without loss of robustness. Importantly, Kriging is fully used to efficiently implement RBIS. It not only serves as a surrogate to classify samples as we all know, but also guides the procedure to determine the optimal radius, with which RBIS would reduce samples to the highest degree. When used as a surrogate, Kriging is established through active learning, where the previously evaluated points to determine the optimal radius are reused. The robustness and efficiency of the present method are validated by five representative examples, where the present method is compared mainly with two fundamental reliability methods based on active learning Kriging. 展开更多
关键词 structural reliability simulation radial-based importance sampling Kriging active learning
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