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岩质高边坡稳定性支持向量机预测方法研究 被引量:1
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作者 缪宁 《云南地质》 2011年第4期487-489,475,共4页
提出一种基于支持向量机的岩质边坡稳定性预测方法。该方法地很好的表达了岩质边坡稳定性与其影响因素之间的非线性映射关系,并应用该方法建立了相应的模型。预测结果表明,利用该方法进行岩质边坡稳定性预测是可行的、有效的。
关键词 岩质边坡稳定性 高维、非线性、复杂 向量预测 广度优选
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基于粒子群优化算法支持向量回归预测法的大电网电压稳定在线评估方法 被引量:4
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作者 李帅虎 赵翔 蒋昀宸 《湖南电力》 2022年第5期22-28,共7页
提出基于粒子群优化算法支持向量回归预测法(particle swarm optimization support vector regression,PSO-SVR)的大电网电压稳定在线评估方法,将传统基于深度神经网络(deep neural networks,DNN)模型的电压稳定评估方法改进为PSO优化过... 提出基于粒子群优化算法支持向量回归预测法(particle swarm optimization support vector regression,PSO-SVR)的大电网电压稳定在线评估方法,将传统基于深度神经网络(deep neural networks,DNN)模型的电压稳定评估方法改进为PSO优化过的SVR模型,对阻抗模裕度指标进行预测。该方法利用了SVR模型具有学习能力强、泛化错误率低的优点,在小样本的情况下也可以很好地学习到样本中的特征。同时克服SVR模型对于参数调节和函数选择非常敏感的问题,利用PSO算法对SVR模型的超参数进行优化选择,可以让SVR模型更好地学习到电网运行数据和阻抗模裕度值之间的非线性关系。最后,该方法在IEEE 118节点系统进行验证,并与基于DNN模型的评估方法进行比较,验证了其精度水平高于基于DNN模型的方法。 展开更多
关键词 电力系统 静态电压稳定 阻抗模裕度 粒子群优化算 支持向量回归预测
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基于多尺度卷积网络的单幅图像的点法向估计 被引量:1
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作者 冼楚华 刘欣 +1 位作者 李桂清 金烁 《华南理工大学学报(自然科学版)》 EI CAS CSCD 北大核心 2018年第12期1-9,共9页
单幅图片法向量估计是计算机图形学和计算机视觉研究的重要问题之一.在缺少其它三维信息的情况下,由单幅图像预测出对应法向量,对于三维场景重建,三维模型识别,三维语义分割等具有重要意义.为解决这一问题,文中使用多尺度的卷积网络结构... 单幅图片法向量估计是计算机图形学和计算机视觉研究的重要问题之一.在缺少其它三维信息的情况下,由单幅图像预测出对应法向量,对于三维场景重建,三维模型识别,三维语义分割等具有重要意义.为解决这一问题,文中使用多尺度的卷积网络结构,对图像进行端到端的输出预测.该网络由两个层级组成,第1层采用在ImageNet中性能最好的DenseNet分类网络,对输入进行全局处理.第2层级采用全卷积网络结构,对第1层级获得的输出进行进一步的精细预测.实验结果表明,即使不使用其他预处理或后处理步骤,文中提出的网络在单幅图像点法向预测方面仍能取得较理想的结果. 展开更多
关键词 法向量预测 单幅图像 卷积网络
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基于动态权重优化的风电机组齿轮箱轴承温度预测模型
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作者 吴九牛 翟广宇 +2 位作者 李德仓 高德成 蒋维栋 《轴承》 北大核心 2024年第9期100-107,共8页
为准确预测风电机组齿轮箱轴承的温度状态,结合灰色预测GM(1,N)模型、BP神经网络模型和支持向量回归模型,提出了一种动态权重优化的组合预测模型。通过对3种预测模型的理论分析选择了各自合理的模型结构,并用粒子群算法优化模型参数;预... 为准确预测风电机组齿轮箱轴承的温度状态,结合灰色预测GM(1,N)模型、BP神经网络模型和支持向量回归模型,提出了一种动态权重优化的组合预测模型。通过对3种预测模型的理论分析选择了各自合理的模型结构,并用粒子群算法优化模型参数;预处理齿轮箱轴承温度的原始数据后用指数平滑法确定各单一模型的动态权重系数,建立齿轮箱轴承温度的组合模型;通过滑动窗口法统计分析齿轮箱轴承预测温度的残差,判断齿轮箱轴承的运行状态。研究结果表明:组合模型的各项评价指标均优于单一预测模型,决定系数为0.9772,预测效果更加稳定准确,能够及时监测齿轮箱轴承温度的变化情况。 展开更多
关键词 滚动轴承 风力发电机组 温度 预测 灰色系统 神经网络 支持向量回归预测
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SVM method for predicting the thickness of sandstone 被引量:4
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作者 乐友喜 王俊 《Applied Geophysics》 SCIE CSCD 2007年第4期276-281,共6页
The Support Vector Machine (SVM) method can be used to set up a nonlinear function prediction model. It is based on the small sample learning theory. The kernel function can be constructed automatically based on the... The Support Vector Machine (SVM) method can be used to set up a nonlinear function prediction model. It is based on the small sample learning theory. The kernel function can be constructed automatically based on the actual sample data by using the SVM method. As a result, the function not only gets a higher fit precision but is also better generalized. The frequency spectrum and seismic waveform are related by Fourier transform, so they are two different forms of the same physical phenomenon. The variety of waveform character reflects stratigraphic differences and frequency spectrum differences reflect the variation of lithology, fluid composition, and formation thickness. It directly predicts sandstone thickness using the seismic waveform. This not only fully utilizes the seismic information but also greatly increases the accuracy of the prediction. Model examples and actual applications show the applicability of this method. 展开更多
关键词 Reservoir prediction seismic waveform Support Vector Machine GENERALIZATION
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Support vector machine forecasting method improved by chaotic particle swarm optimization and its application 被引量:11
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作者 李彦斌 张宁 李存斌 《Journal of Central South University》 SCIE EI CAS 2009年第3期478-481,共4页
By adopting the chaotic searching to improve the global searching performance of the particle swarm optimization (PSO), and using the improved PSO to optimize the key parameters of the support vector machine (SVM) for... By adopting the chaotic searching to improve the global searching performance of the particle swarm optimization (PSO), and using the improved PSO to optimize the key parameters of the support vector machine (SVM) forecasting model, an improved SVM model named CPSO-SVM model was proposed. The new model was applied to predicting the short term load, and the improved effect of the new model was proved. The simulation results of the South China Power Market’s actual data show that the new method can effectively improve the forecast accuracy by 2.23% and 3.87%, respectively, compared with the PSO-SVM and SVM methods. Compared with that of the PSO-SVM and SVM methods, the time cost of the new model is only increased by 3.15 and 4.61 s, respectively, which indicates that the CPSO-SVM model gains significant improved effects. 展开更多
关键词 chaotic searching particle swarm optimization (PSO) support vector machine (SVM) short term load forecast
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On-line least squares support vector machine algorithm in gas prediction 被引量:21
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作者 ZHAO Xiao-hu WANG Gang ZHAO Ke-ke TAN De-jian 《Mining Science and Technology》 EI CAS 2009年第2期194-198,共5页
Traditional coal mine safety prediction methods are off-line and do not have dynamic prediction functions.The Support Vector Machine(SVM) is a new machine learning algorithm that has excellent properties.The least squ... Traditional coal mine safety prediction methods are off-line and do not have dynamic prediction functions.The Support Vector Machine(SVM) is a new machine learning algorithm that has excellent properties.The least squares support vector machine(LS-SVM) algorithm is an improved algorithm of SVM.But the common LS-SVM algorithm,used directly in safety predictions,has some problems.We have first studied gas prediction problems and the basic theory of LS-SVM.Given these problems,we have investigated the affect of the time factor about safety prediction and present an on-line prediction algorithm,based on LS-SVM.Finally,given our observed data,we used the on-line algorithm to predict gas emissions and used other related algorithm to compare its performance.The simulation results have verified the validity of the new algorithm. 展开更多
关键词 LS-SVM GAS on-line learning PREDICTION
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Pattern recognition and prediction study of rock burst based on neural network 被引量:2
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作者 LI Hong 《Journal of Coal Science & Engineering(China)》 2010年第4期347-351,共5页
Many monitoring measures were used in the production field for predicting rockburst.However, predicting rock burst according to complicated observation data is alwaysa pressing problem in this research field.Though th... Many monitoring measures were used in the production field for predicting rockburst.However, predicting rock burst according to complicated observation data is alwaysa pressing problem in this research field.Though the critical value method gets extensiveapplication in practice, it stresses only on the superficial change of data and overlooks alot of features of rock burst and useful information that is concealed and hidden in the observationtime series.Pattern recognition extracts the feature value of time domain, frequencydomain and wavelet domain in observation time series to form Multi-Feature vectors,using Euclidean distance measure as the separable criterion between the same typeand different type to compress and transform feature vectors.It applies neural network asa tool to recognize the danger of rock burst, and uses feature vectors being compressedto carry out training and studying.It is proved by test samples that predicting precisionshould be prior to such traditional predicting methods as pattern recognition and critical indicatormethod. 展开更多
关键词 rock burst multi-feature pattern recognition neural network
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Protein domain boundary prediction by combining support vector machine and domain guess by size algorithm
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作者 董启文 Wang +2 位作者 Xiaolong Lin Lei 《High Technology Letters》 EI CAS 2007年第1期74-78,共5页
Successful prediction of protein domain boundaries provides valuable information not only for the computational structure prediction of muhi-domain proteins but also for the experimental structure determination. A nov... Successful prediction of protein domain boundaries provides valuable information not only for the computational structure prediction of muhi-domain proteins but also for the experimental structure determination. A novel method for domain boundary prediction has been presented, which combines the support vector machine with domain guess by size algorithm. Since the evolutional information of multiple domains can be detected by position specific score matrix, the support vector machine method is trained and tested using the values of position specific score matrix generated by PSI-BLAST. The candidate domain boundaries are selected from the output of support vector machine, and are then inputted to domain guess by size algorithm to give the final results of domain boundary, prediction. The experimental results show that the combined method outperforms the individual method of both support vector machine and domain guess by size. 展开更多
关键词 domain boundary prediction support vector machine domain guess by size positionspecific score matrix
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SEQUENCE-BASED PROTEIN-PROTEIN INTERACTION PREDICTION VIA SUPPORT VECTOR MACHINE 被引量:1
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作者 Yongcui WANG Jiguang WANG +1 位作者 Zhixia YANG Naiyang DENG 《Journal of Systems Science & Complexity》 SCIE EI CSCD 2010年第5期1012-1023,共12页
This paper develops sequence-based methods for identifying novel protein-protein interactions (PPIs) by means of support vector machines (SVMs). The authors encode proteins ont only in the gene level but also in t... This paper develops sequence-based methods for identifying novel protein-protein interactions (PPIs) by means of support vector machines (SVMs). The authors encode proteins ont only in the gene level but also in the amino acid level, and design a procedure to select negative training set for dealing with the training dataset imbalance problem, i.e., the number of interacting protein pairs is scarce relative to large scale non-interacting protein pairs. The proposed methods are validated on PPIs data of Plasmodium falciparum and Escherichia coli, and yields the predictive accuracy of 93.8% and 95.3%, respectively. The functional annotation analysis and database search indicate that our novel predictions are worthy of future experimental validation. The new methods will be useful supplementary tools for the future proteomics studies. 展开更多
关键词 Imbalance problem protein-protein interactions sequence-based support vector machine.
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