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1.
改进的SVM算法及其在故障诊断中的应用研究   总被引:1,自引:1,他引:0  
介绍了支持向量机用于解决模式分类问题的基本原理,在对传统的多分类方法OVO(one—versus—one)深入分析的基础上,针对其存在的不可分类区问题,提出了一种改进的模式分类方法KSVM(KNN—SVM),将k-近邻方法嵌入到SVM算法中解决不可分类区问题,进一步提高了分类准确率。应用KSVM分类方击进行模拟电路的故障诊断,实验结果验证了该方法的有效性和实用性。  相似文献   
2.
一种基于morlet小波核的约简支持向量机   总被引:7,自引:0,他引:7  
针对支持向量机(SVM)的训练数据量仅局限于较小样本集的问题,结合Morlet小波核函数,提出了一种基于Morlet小波核的约倚支持向量机(MWRSVM—DC).算法的核心是通过密度聚类寻找聚类中每个簇的边缘点作为约倚集合,并利用该约倚集合寻找支持向量.实验表明,利用小波核,该算法不仅提高了分类的准确率,而且提高了整体分类效率.  相似文献   
3.
基于支持向量化与混沌搜索的新型直线电机参数优化   总被引:3,自引:2,他引:1  
以一种新型圆筒直线电机为研究对象,推导出了适合其结构特征的电磁方程;采用变分方法,建立了电机轴对称涡流场的有限元模型;通过仿真计算,得出了电机的启动推力和电流;样机试制和输出性能测试表明了有限元模型的正确性.基于正交和随机混合试验设计方法安排有限元仿真试验,获得了用于非线性电磁建模的样本空间;通过支持向量机对结构参数与计算结果的输入输出关系进行非参数回归建模,然后用混沌搜索方法对回归函数进行优化,得出最优目标结构参数.有限元仿真验证表明,基于支持向量机和混沌搜索的优化方法用于研究新型直线电机电磁结构参数是可行的.  相似文献   
4.
To improve the training speed of support vector machine(SVM),a method called improved center distance ratio method(ICDRM)with determining thresholds automatically is presented here without reduce the identification rate.In this method border vectors are chosen from the given samples by comparing sample vectors with center distance ratio in advance.The number of training samples is reduced greatly and the training speed is improved.This method is used to the identification for license plate characters.Experimental results show that the improved SVM method-ICDRM does well at identification rate and training speed.  相似文献   
5.
为了进行视频结构化和视频内容分析,需要准确有效地提取视频镜头的边界信息.为此提出了一种利用支持向量机(SVM)学习压缩域特征的算法进行镜头边界检测,只需简单译码即可得到MPEG1/2等各类视频流压缩域的特征信息.经TRECVID2005镜头边界检测集的评测,该算法在保证查全率和检测精度的情况下获得了满意的效果.  相似文献   
6.
支持向量机在模式识别中的应用   总被引:3,自引:0,他引:3  
针对传统神经网络存在网络结构难于确定、过学习以及局部极小等问题,研究了基于支持向量机(SVM)的模式识别问题。通过对棋盘这种典型非线性二值问题的分类研究,分析了支持向量机的分类与泛化能力。支持向量机在分类和泛化能力方面远远优于传统神经网络。最后将支持向量机用于对两类飞机目标的分类识别,通过多组蒙特卡罗试验,获得了较好的识别结果。支持向量机在目标识别中有巨大潜力和广阔前景。  相似文献   
7.
A method of applying support vector machine (SVM) in speech recognition was proposed, and a speech recognition system for mandarin digits was built up by SVMs. In the system, vectors were linearly extracted from speech feature sequence to make up time-aligned input patterns for SVM, and the decisions of several 2-class SVM classifiers were employed for constructing an N-class classifier. Four kinds of SVM kernel functions were compared in the experiments of speaker-independent speech recognition of mandarin digits. And the kernel of radial basis function has the highest accurate rate of 99.33 %, which is better than that of the baseline system based on hidden Markov models (HMM) (97.08%). And the experiments also show that SVM can outperform HMM especially when the samples for learning were very limited.  相似文献   
8.
An improved approach based on support vector machine (SVM) called the center distance ratio method is presented for license plate character recognition. First the support vectors are pre-extraeted. A minimal set called the margin vector set, which contains all support vectors, is extracted. These margin vectors compose new training data and construct the classifier by using the general SVM optimized. The experimental resuhs show that the improved SVM method does well at correct rate and training speed.  相似文献   
9.
To acquire human operation skill based on force sense, element contact form (ECF) is proposed to describe contact-condition firstly. The skill is modeled as a sequence of discrete ECFs. Since different ECF has different force distribution, a support vector machine classifier is built to identify the contact conditions according to the force signal. Finally, the robot can obtain the skill from the human demonstration.  相似文献   
10.
In order to solve the problems of small sample over-fitting and local minima when neural networks learn online, a novel method of predicting network bandwidth based on support vector machines(SVM) is proposed. The prediction and learning online will be completed by the proposed moving window learning algorithm (MWLA). The simulation research is done to validate the proposed method, which is compared with the method based on neural networks.  相似文献   
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