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1.
基于曲率模态和支持向量机的结构损伤位置两步识别方法   总被引:1,自引:0,他引:1  
刘龙  孟光 《工程力学》2006,23(Z1):35-39
支持向量机是一种基于统计学习理论的机器学习算法,能够较好的解决小样本的学习问题。介绍了支持向量机分类和回归算法,将其应用于梁结构的损伤诊断中。以曲率模态参数作为损伤识别指标,提出了基于支持向量机的结构损伤位置两步识别方法:首先根据支持向量机分类算法的概率估计找到可能的损伤位置,重新构造训练样本;然后利用支持向量机回归算法计算精确的损伤位置。通过对悬臂梁仿真计算进行了验证,结果表明:支持向量机在结构损伤诊断领域中具有较好的应用前景。  相似文献   

2.
为支持向量回归机提供了一个新的光滑函数,即运用三次样条函数和复合函数的方法,得到一种新的光滑支持向量回归机——三次样条光滑支持向量回归机(TSSSVR).对该支持向量回归机的光滑函数进行了逼近性能和收敛性的分析,并说明该光滑函数比以往的光滑函数具有更高的逼近精度和收敛速度.  相似文献   

3.
小样本数据的支持向量机回归模型参数及预测区间研究   总被引:6,自引:0,他引:6  
陈果  周伽 《计量学报》2008,29(1):92-96
支持向量机是由统计学习理论发展起来的机器学习算法,它从结构风险最小化的角度保证了模型的最大泛化能力.文中运用支持向量机进行小样本数据回归分析研究.首先利用推广性的界理论指导支持向量机回归模型参数的选取,以保证模型具有最大的推广能力;其次,运用基于正态分布和基于t分布的两种区间预测方法进行了预测值的区间估计;最后,利用模拟序列和真实的航空发动机油样光谱分析数据作为实验数据,建立了支持向量机回归分析模型,并与最小二乘法进行了比较.结果表明,所提出的支持向量机模型参数选取和区间估计方法适用于小样本数据的回归分析,具有较高的预测精度.  相似文献   

4.
支持向量机是基于统计学习理论的一种模式识别方法,近年来以其优良的特性引起了研究者的广泛关注,已经成为一个十分活跃的研究领域。本文系统介绍了支持向量机的理论及应用方法,讨论了支持向量机中核函数的选择问题。然后对二类SVM实现算法和多类SVM实现算法进行分析,总结其性能与优缺点,最后指出SVM中待解决的一些同题和日后的研究方向。  相似文献   

5.
蚁群支持向量机在内燃机故障诊断中的应用研究   总被引:1,自引:1,他引:0  
针对目前支持向量机参数选择时人为选择的盲目性,将具有良好优化性能的蚁群优化技术应用到支持向量机惩罚函数和核函数参数的优化,提出了蚁群优化支持向量机方法。根据内燃机气门振动信号实测数据,建立了基于蚁群优化支持向量机的内燃机气门间隙故障诊断模型,并与基于遗传支持向量机和反向传播神经网络算法的模型比较。结果表明:应用蚁群优化支持向量机建立的内燃机气门间隙故障诊断模型无论从学习效率还是故障识别准确性上都优于应用另外两种算法建立的模型,能够有效地进行内燃机的故障诊断。  相似文献   

6.
基于支持向量机改进算法的船舶类型识别研究   总被引:3,自引:0,他引:3       下载免费PDF全文
利用船舶目标辐射噪声DEMON谱特征,采用改进的支持向量机算法,实现了对船舶目标的分类识别研究。针对支持向量机算法对噪声比较敏感和最优分类面求解时约束较多不利于支持向量机最优分类面寻优的问题,在保持支持向量稀疏性和应用径向基核函数的条件下,对支持向量机算法在松弛变量和决策函数两方面进行了改进,提出了基于径向基核函数的齐次决策二阶损失函数支持向量机改进算法,并应用于利用船舶目标辐射噪声DEMON谱进行船舶目标类型分类识别实验。理论分析、数据仿真与实验结果表明,该改进算法实现了在二次规划中的较少约束条件下最优分类面求解,具有模型参数寻优空间广阔、总体分类性能优的特点,其分类性能优于原支持向量机算法,是一种适合于船舶辐射噪声DENOM分类识别的有效的支持向量机改进算法。  相似文献   

7.
本文提出了一种基于支持向量机的坦克识别算法。在对图像预处理之后,运用颜色和纹理信息进行分割,采用基于数学形态学的算法求得边缘像素,提取具有RST不变性的轮廓特征向量,输入支持向量机进行训练和识别。将支持向量机与传统的人工神经网络的算法进行了对比实验,实验表明基于支持向量机的坦克识别算法具有更好的性能。  相似文献   

8.
针对传统支持向量机回归模型应用在红外甲烷传感器测量数据处理时出现预测精度低的问题,提出了一种基于灰狼优化算法的支持向量机回归模型。该模型在传统支持向量机的基础上,利用灰狼优化算法自适应搜索特征空间来选择最佳特征组合,经过循环比较,能快速、准确地搜索到最优的惩罚因子C与gamma参数。用实验室研制的红外甲烷传感器对0~5.05%浓度范围的标准甲烷气体进行测量后,建立了3种SVM回归模型,并进行对比。结果表明,采用灰狼优化算法建立的支持向量机回归模型其绝对误差和相对误差小,精度高。  相似文献   

9.
基于多输出支持向量回归机的有限元模型修正   总被引:2,自引:1,他引:1       下载免费PDF全文
为了克服神经网络以及单输出支持向量回归算法在有限元模型修正中的不足,提出了基于多输出支持向量回归算法的有限元模型修正方法。根据5-折交叉验证法选择支持向量回归机的参数,用均匀试验设计法构造样本,联合结构的动力和静力响应数据作为输入,多个设计参数作为输出,以支持向量回归机逼近输入输出二者之间的非线性映射关系,然后利用支持向量回归机的泛化推广能力,求解设计参数的目标值。空间网格结构数值模型的分析结果表明,该方法能同时修正多个设计参数,在少量样本的情况下具有较高的修正精度,为有限元模型修正提供了一种新的探索。  相似文献   

10.
针对小样本步态数据引起的分类器泛化能力差的问题,提出了基于支持向量机的步态分类方法.采集了24名青年和24名老年受试者的步态数据,提取24个步态特征训练支持向量机,采用交叉验证方法评估分类器的泛化性能.结果表明,本文提出的方法能够有效地对小样本步态数据分类,并且具有良好的泛化性.不同的核函数对分类性能影响较小.与传统反向传播学习算法的神经网络分类器进行了比较,支持向量机分类性能明显优于传统反向传播学习算法的神经网络.支持向量机在步态分类中具有广泛的应用前景.  相似文献   

11.
支持向量回归算法在梁结构损伤诊断中的应用研究   总被引:5,自引:3,他引:5  
刘龙  孟光 《振动与冲击》2006,25(3):99-100,126
支持向量机算法具有很优秀的回归特性,所以将其应用于梁结构的损伤诊断方面。以模态频率作为特征参数,训练支持向量机实现对损伤的定位和程度标识,并通过对悬臂梁的损伤识别仿真计算进行了验证。结果表明:支持向量机在结构损伤诊断领域中具有很好的应用前景。  相似文献   

12.
The statistical learning classification techniques have been successfully applied to statistical process control problems. In this paper, we proposed a one‐sided control chart based on support vector machines (SVMs) and differential evolution (DE) algorithm to monitor a process with multivariate quality characteristics. The SVM classifier provides a continuous distance from the boundary, and the DE algorithm is used to obtain the optimal parameters of the SVM model by minimizing mean absolute error (MAE). The average run length of the proposed chart is computed using the Monte Carlo simulation approach. Several simulated cases are conducted using a multivariate normal distribution with 10 and 20 dimensions and three different process shift scenarios. In addition, we consider two non‐normal distribution cases. The ARL performance of the proposed chart is better than the distance‐based SVM chart. A real example is used to illustrate the application of the proposed control chart.  相似文献   

13.
The operation complexity of the distribution system increases as a large number of distributed generators (DG) and electric vehicles were introduced, resulting in higher demands for fast online reactive power optimization. In a power system, the characteristic selection criteria for power quality disturbance classification are not universal. The classification effect and efficiency needs to be improved, as does the generalization potential. In order to categorize the quality in the power signal disturbance, this paper proposes a multi-layer severe learning computer auto-encoder to optimize the input weights and extract the characteristics of electric power quality disturbances. Then, a multi-label classification algorithm based on rating is proposed to understand the relationship between the labels and identify the various power quality disturbances. The two algorithms are combined to construct a multi-label classification model based on a multi-level extreme learning machine, and the optimal network structure of the multi-level extreme learning machine as well as the optimal multi-label classification threshold are developed. The proposed method can be used to classify the single and compound power quality disturbances with improved classification effect, reliability, robustness, and anti-noise performance, according to the experimental results. The hamming loss obtained by the proposed algorithm is about 0.17 whereas ML-RBF, SVM and ML-KNN schemes have 0.28, 0.23 and 0.22 respectively at a noise intensity of 20 dB. The average precision obtained by the proposed algorithm 0.85 whereas the ML-RBF, SVM and ML-KNN schemes indicates 0.7, 0.77 and 0.78 respectively.  相似文献   

14.
Stroke and cerebral haemorrhage are the second leading causes of death in the world after ischaemic heart disease. In this work, a dataset containing medical, physiological and environmental tests for stroke was used to evaluate the efficacy of machine learning, deep learning and a hybrid technique between deep learning and machine learning on the Magnetic Resonance Imaging (MRI) dataset for cerebral haemorrhage. In the first dataset (medical records), two features, namely, diabetes and obesity, were created on the basis of the values of the corresponding features. The t-Distributed Stochastic Neighbour Embedding algorithm was applied to represent the high-dimensional dataset in a low-dimensional data space. Meanwhile,the Recursive Feature Elimination algorithm (RFE) was applied to rank the features according to priority and their correlation to the target feature and to remove the unimportant features. The features are fed into the various classification algorithms, namely, Support Vector Machine (SVM), K Nearest Neighbours (KNN), Decision Tree, Random Forest, and Multilayer Perceptron. All algorithms achieved superior results. The Random Forest algorithm achieved the best performance amongst the algorithms; it reached an overall accuracy of 99%. This algorithm classified stroke cases with Precision, Recall and F1 score of 98%, 100% and 99%, respectively. In the second dataset, the MRI image dataset was evaluated by using the AlexNet model and AlexNet + SVM hybrid technique. The hybrid model AlexNet + SVM performed is better than the AlexNet model; it reached accuracy, sensitivity, specificity and Area Under the Curve (AUC) of 99.9%, 100%, 99.80% and 99.86%, respectively.  相似文献   

15.
Epilepsy is a type of brain disorder that causes recurrent seizures. It is the second most common neurological disease after Alzheimer’s. The effects of epilepsy in children are serious, since it causes a slower growth rate and a failure to develop certain skills. In the medical field, specialists record brain activity using an Electroencephalogram (EEG) to observe the epileptic seizures. The detection of these seizures is performed by specialists, but the results might not be accurate due to human errors; therefore, automated detection of epileptic pediatric seizures might be the optimal solution. This paper investigates the detection of epileptic seizures by applying supervised machine learning techniques. The techniques applied on the data of patients with ages seven years and below from children’s hospital boston massachusetts institute of technology (CHB-MIT) scalp EEG database of epileptic pediatric signals. A group of Naïve Bayes (NB), Support vector machine (SVM), Logistic regression (LR), k-nearest neighbor (KNN), Linear discernment (LD), Decision tree (DT), and ensemble learning methods were applied to the classification process. The results demonstrated the outperformance of the present study by achieving 100% for all parameters using the Ensemble learning model in contrast to state-of-the-art studies in the literature. Similarly, the SVM model achieved performance with 98.3% for sensitivity, 97.7% for specificity, and 98% for accuracy. The results of the LD and LR models reveal the lower performance i.e., the sensitivity at 66.9%–68.9%, specificity at 73.5%–77.1%, and accuracy at 70.2%–73%.  相似文献   

16.
Due to its outstanding ability in processing large quantity and high-dimensional data, machine learning models have been used in many cases, such as pattern recognition, classification, spam filtering, data mining and forecasting. As an outstanding machine learning algorithm, K-Nearest Neighbor (KNN) has been widely used in different situations, yet in selecting qualified applicants for winning a funding is almost new. The major problem lies in how to accurately determine the importance of attributes. In this paper, we propose a Feature-weighted Gradient Decent K-Nearest Neighbor (FGDKNN) method to classify funding applicants in to two types: approved ones or not approved ones. The FGDKNN is based on a gradient decent learning algorithm to update weight. It updatesthe weight of labels by minimizing error ratio iteratively, so that the importance of attributes can be described better. We investigate the performance of FGDKNN with Beijing Innofund. The results show that FGDKNN performs about 23%, 20%, 18%, 15% better than KNN, SVM, DT and ANN, respectively. Moreover, the FGDKNN has fast convergence time under different training scales, and has good performance under different settings.  相似文献   

17.
Human gait recognition (HGR) has received a lot of attention in the last decade as an alternative biometric technique. The main challenges in gait recognition are the change in in-person view angle and covariant factors. The major covariant factors are walking while carrying a bag and walking while wearing a coat. Deep learning is a new machine learning technique that is gaining popularity. Many techniques for HGR based on deep learning are presented in the literature. The requirement of an efficient framework is always required for correct and quick gait recognition. We proposed a fully automated deep learning and improved ant colony optimization (IACO) framework for HGR using video sequences in this work. The proposed framework consists of four primary steps. In the first step, the database is normalized in a video frame. In the second step, two pre-trained models named ResNet101 and InceptionV3 are selected and modified according to the dataset's nature. After that, we trained both modified models using transfer learning and extracted the features. The IACO algorithm is used to improve the extracted features. IACO is used to select the best features, which are then passed to the Cubic SVM for final classification. The cubic SVM employs a multiclass method. The experiment was carried out on three angles (0, 18, and 180) of the CASIA B dataset, and the accuracy was 95.2, 93.9, and 98.2 percent, respectively. A comparison with existing techniques is also performed, and the proposed method outperforms in terms of accuracy and computational time.  相似文献   

18.
为了找到汽轮机在不同负荷下的最优初压,利用改进的共生生物搜索(FSOS)算法和极限学习机(ELM)建立热耗率预测模型,并与BP神经网络、共生生物搜索(SOS)算法优化ELM和FSOS算法优化支持向量机(SVM)等进行了比较。然后,在该模型的基础上用FSOS算法对主蒸汽压力和主蒸汽流量进行优化,使其在各负荷下的热耗率最低。最后,通过优化后的主蒸汽压力拟合出一条最优初压曲线,并与厂家设计的滑压运行曲线进行对比。结果表明:按照最优初压曲线运行,热耗率平均下降约58.51 kJ·(kW· h)-1,提高了机组能量的转换效率,对汽轮机经济运行有着显著的效果。  相似文献   

19.
针对电机故障诊断问题,设计一种新型的一维卷积神经网络结构(1D-CNN),提出一种基于声信号和1DCNN的电机故障诊断方法。为了验证1D-CNN算法在电机故障识别领域的有效性,以一组空调故障电机作为实验对象,搭建电机故障诊断平台,对4种状态的空调电机进行声信号采集实验,制作电机故障声信号数据集,并运用1DCNN算法对数据集进行分类,计算出基于该算法的电机故障识别准确率。实验结果表明,1D-CNN算法作为一种新型结构深度学习算法,能够对电机故障声信号进行很好分类,分类准确率高于FFT-BP、SVM、FFT-SAE等算法。为了探究1D-CNN算法内在机制,还对1D-CNN算法性能进行t-SNE可视化分析。  相似文献   

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