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为提高下肢表面肌电信号步态识别的识别精度和计算效率,采用一种基于高斯核函数优化正则化超限学习机(GKF-RELM)算法,对肌电信号提取时域、频域和非线性动力学三类特征并分别计算步态识别率,运用Fisher判别函数分析所提特征的可分性,得到多类特征的融合特征作为输入数据对分类器进行训练,再用训练好的分类器进行步态识别,从识别率和计算时间两方面,分别与支持向量机(SVM)和深度神经网络(DNN)方法进行了对比分析.结果表明,基于Fisher判别可分性指标确定的多类特征组合,能得到最优识别效果,并在提高分类精度的同时,优化了计算效率.此外,GKF-RELM方法的识别率也优于传统的ELM方法. 相似文献
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《信息技术》2017,(10):141-145
支持向量机(SVM)在雷达目标高分辨距离像(HRRP)识别中可获得较高的正确识别率和更好的泛化性能,然而其性能很大程度上取决于其参数包括核函数参数σ2和惩罚因子C的合理选择。所以利用粒子群优化算法(PSO)全局搜索能力强的优点来搜寻最优参数,并针对粒子群优化易陷入局部最优的问题,提出一种惯性权重自适应改变的改进方法。通过对雷达目标高分辨率距离像(HRRP)的识别实验发现,利用PSO优化SVM参数的方法克服了传统SVM存在的很难精确找到最优参数的缺点,识别准确率也有很大提高;同时惯性权重自适应改变的方法也有效解决了PSO优化的"早熟"问题,大大缩短参数寻优时间。 相似文献
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为提高对外骨骼机器人步态检测的准确性,提出了一种基于改进麻雀搜索算法(ISSA)优化支持向量机(SVM)的外骨骼机器人步态检测新方法,即在麻雀搜索算法(SSA)中引入正余弦算法和Levy飞行策略以提高算法的寻优性能,进而实现SVM参数的合理优化,从而达到提升外骨骼机器人步态检测的准确性。4个典型测试函数的测试结果表明,ISSA算法在收敛精度、收敛速度和收敛稳定性上比其他几种方法更优;外骨骼机器人步态检测实例结果表明,基于ISSA-SVM的步态检测准确率得到了提升,可以实现外骨骼机器人步态的有效检测。 相似文献
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为了提高表面肌电信号(surface Electromyographic signal, sEMG)手势动作识别的准确率,本文提出基于双权重粒子群算法(Particle Swarm Optimization, PSO)优化支持向量机(Support Vector Machine, SVM)的分类模型(DWPSO-SVM)。针对传统PSO在参数寻优时易陷入“早熟”问题,进一步提高粒子寻优能力,本文在标准PSO的基础上引入约束因子结合同向更新策略用于速度约束,有效的提高了粒子的寻优能力并缓解了“早熟”现象;其次,分析了多种权重更新策略对惯性权重和约束因子的影响;最终,采用非线性更新策略结合DWPSO优化SVM模型构建特征分类模型。实验表明,本文提出的DWPSO-SVM模型能够有效完成sEMG手势动作识别任务。 相似文献
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基于人工蜂群算法的支持向量机参数优化及应用 总被引:2,自引:1,他引:1
为了解决常用的支持向量机(SVM)参数优化方法在寻优过程不同程度的陷入局部最优解的问题,提出一种基于人工蜂群(ABC)算法的SVM参数优化方法。将SVM的惩罚因子和核函数参数作为食物源位置,分类正确率作为适应度,利用ABC算法寻找适应度最高的食物源位置。利用4个标准数据集,将其与遗传(GA)算法、蚁群(ACO)算法、标准粒子群(PSO)算法优化的SVM进行性能比较,结果表明,本文方法能克服局部最优解,获得更高的分类正确率,并在小数目分类问题上有效降低运行时间。将本文方法运用到计算机笔迹鉴别,对提取的笔迹特征进行分类,与GA算法、ACO算法、PSO算法优化的SVM相比,得到了更高的分类正确率。 相似文献
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针对非线性SVM及LDA算法在肌电信号手势识别应用上的合理性问题进行实验,比较新型非线性支持向量机(SVM)分类方法和实际应用中常用的线性判别分析(LDA)在肌电图手势识别上的优劣。首先采用1到6不同数量的电极采集3组不同的手臂动作的前臂肌电信号,记录数据。然后,通过计算机编写算法程序对比SVM和LDA两种方法在不同电极数量下的肌电手势识别的准确率。最后得出结论,2种算法的手势识别率与肌电电极数量密切相关,根据电极数选择合适的分类算法。分析表明,该实验在减少电极数量情况下对手势识别算法的选择有重要意义。 相似文献
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提出一种基于粒子群优化算法的支持向量机网络,并把它应用到语音情感识别系统中。依据情感的维度空间模型,研究分析情感语音数据的韵律特征与音质特征。利用粒子群优化算法(PSO)训练网络的超参数以优化支持向量机模型,可快速地实现网络的收敛。最后在实验中比较线性核函数SVM、径向基核函数SVM与粒子群优化径向基SVM分别用于语音情感识别的识别率,结果显示粒子群优化径向基核SVM模型用于语音情感识别能获得明显的识别性能的提升。 相似文献
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粒子群优化的最小二乘支持向量机在通信装备故障预测中的应用 总被引:1,自引:0,他引:1
提出了一种通信装备故障预测的智能算法.该方法将粒子群算法(PSO)和最小二乘支持向量机(LS-SVM)算法相结合,采用PSO算法优化LS-SVM的参数,克服了人为参数选择的盲目性,在全局优化与收敛速度方面具有较大优势.仿真实验表明,相比BP神经网络、未经优化的支持向量机(SVM)和LS-SVM模型,经PSO算法优化后的LS-SVM有更高的预测精度和运算速度,具有较好的有效性和可行性. 相似文献
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Support vector machines for automated gait classification 总被引:8,自引:0,他引:8
Ageing influences gait patterns causing constant threats to control of locomotor balance. Automated recognition of gait changes has many advantages including, early identification of at-risk gait and monitoring the progress of treatment outcomes. In this paper, we apply an artificial intelligence technique [support vector machines (SVM)] for the automatic recognition of young-old gait types from their respective gait-patterns. Minimum foot clearance (MFC) data of 30 young and 28 elderly participants were analyzed using a PEAK-2D motion analysis system during a 20-min continuous walk on a treadmill at self-selected walking speed. Gait features extracted from individual MFC histogram-plot and Poincaré-plot images were used to train the SVM. Cross-validation test results indicate that the generalization performance of the SVM was on average 83.3% (+/-2.9) to recognize young and elderly gait patterns, compared to a neural network's accuracy of 75.0+/-5.0%. A "hill-climbing" feature selection algorithm demonstrated that a small subset (3-5) of gait features extracted from MFC plots could differentiate the gait patterns with 90% accuracy. Performance of the gait classifier was evaluated using areas under the receiver operating characteristic plots. Improved performance of the classifier was evident when trained with reduced number of selected good features and with radial basis function kernel. These results suggest that SVMs can function as an efficient gait classifier for recognition of young and elderly gait patterns, and has the potential for wider applications in gait identification for falls-risk minimization in the elderly. 相似文献
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Yu Ying Wang Xiaolong Liu Bingquan 《电子科学学刊(英文版)》2005,22(5):550-557
This letter adopts a GA (Genetic Algorithm) approach to assist in learning scaling of features that are most favorable to SVM (Support Vector Machines) classifier, which is named as GA-SVM. The relevant coefficients of various features to the classification task, measured by real-valued scaling, are estimated efficiently by using GA. And GA exploits heavy-bias operator to promote sparsity in the scaling of features. There are many potential benefits of this method: Feature selection is performed by eliminating irrelevant features whose scaling is zero, an SVM classifier that has enhanced generalization ability can be learned simultaneously. Experimental comparisons using original SVM and GA-SVM demonstrate both economical feature selection and excellent classification accuracy on junk e-mail recognition problem and Internet ad recognition problem. The experimental results show that comparing with original SVM classifier, the number of support vector decreases significantly and better classification results are achieved based on GA-SVM. It also demonstrates that GA can provide a simple, general, and powerful framework for tuning parameters in optimal problem, which directly improves the recognition performance and recognition rate of SVM. 相似文献
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Yi-Hung Liu Han-Pang Huang Chang-Hsin Weng 《Mechatronics, IEEE/ASME Transactions on》2007,12(3):253-264
Electromyographic (EMG) signals recognition is a complex pattern recognition problem due to its property of large variations in signals and features. This paper proposes a novel EMG classifier called cascaded kernel learning machine (CKLM) to achieve the goal of high-accuracy EMG recognition. First, the EMG signals are acquired by three surface electrodes placed on three different muscles. Second, EMG features are extracted by autoregressive model (ARM) and EMG histogram. After the feature extraction, the CKLM is performed to classify the features. CKLM is composed of two different kinds of kernel learning machines: generalized discriminant analysis (GDA) algorithm and support vector machine (SVM). By using GDA, both the goals of the dimensionality reduction of input features and the selection of discriminating features, named kernel FisherEMG, can be reached. Then, SVM combined with one-against-one strategy is executed to classify the kernel FisherEMG. By cascading SVM with GDA, the input features will be nonlinearly mapped twice by radial-basis function (RBF). As a result, a linear optimal separating hyperplane can be found with the largest margin of separation between each pair of postures' classes in the implicit dot product feature space. In addition, we develop a digital signal processor (DSP)-based EMG classification system for the control of a multi-degrees-of-freedom prosthetic hand for the practical implementation. Based on the clinical experiments, the results show that the proposed CKLM is superior to other frequently used methods, such as k-nearest neighbor algorithm, multilayer neural network, and SVM. The best EMG recognition rate 93.54% is obtained by CKLM. 相似文献
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Elham Ghazizadeh Dariush Abbasi‐moghadam Hossein Nezamabadi‐pour 《International Journal of Communication Systems》2019,32(2)
Cognitive radio (CR) networks have emerged recently to address the problem of spectrum scarcity. As reliable spectrum sensing (SS) is vital in low signal‐to‐noise ratio (SNR) for CR networks, we propose a novel method of enhancing support vector machines (SVM) classifier named as 2‐Phase SVM for the task of SS in a cooperative sensing structure. In this study, the vectors containing energy levels of primary users (PU) are considered as feature vectors and are fed into the classifier during training and test phase. First, the classifier is trained; afterward, the test feature vectors are labeled as channel available class or channel unavailable class in an online fashion by using 2‐Phase SVM, which is applied during two phases compared with the conventional SVM algorithm. The performance of suggested cooperative SS method is evaluated by receiver operating characteristic (ROC) curve and the functionality of our proposed algorithm is qualified in terms of misclassification error rate in addition to misclassification risk. The results reveal that 2‐Phase SVM outperforms previous methods since it not only increases the classification accuracy and reduces the misclassification risk but also enhances the detection probability. 相似文献
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基于不同Margin的人脸特征选择及识别方法 总被引:1,自引:0,他引:1
Margin在机器学习中具有很重要的意义,基于margin的特征选择方法就是从分类的角度对特征集各特征的权重进行分析。该文对不同的margin进行了分析,提出将sample-margin和hypothesis-margin分别作为特征选择标准对SBS特征选择方法进行改进,然后设计具有最佳超参数的SVM多项式分类器进行人脸识别。实验在FRERT人脸图像库上进行并与Relief特征选择方法进行了比较,对SVM和NN分类器的实验结果也进行了分析。实验结果显示:该文提出的人脸识别特征选择及识别方法是有效、适用的。 相似文献
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目前国内外车型识别方法中基于中网区域特征的研究较少,且分类识别的效率和精度较低。该文在分析中网格栅区域结构特征、中网窗口形状特征及区域纹理特征的基础上,提出基于最优参数搜索的改进型C参数的支持向量分类(C-SVC)车辆中网分类识别方法,该方法采用双角度约束以提高分类的效率和精度,即一方面设计基于马氏距离和a-原则对样本数据进行优化分选,并结合加权判别算法加快支持向量机的训练测试速度,以提高算法泛化效率;另一方面在核函数参数设定过程中,设计了基于先验知识的迭代最优参数搜索算法,以提高分类器的分类识别精度。实验表明,上述车辆中网识别方法检测准确率达到97.53%,具有精度高、误检率低的优点,同时极大优化分类识别效率,能够满足识别分类的实时性要求。 相似文献
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针对支持向量机(SVM)在大规模入侵信号分类时存在的局限性,提出了一种改进的SVM信号识别方法。该方法首先采用粒子群优化算法(PSO)来生成多样化的初始位置,然后利用灰狼优化算法(GWO)更新离散搜索空间中样本的当前位置,获得最优特征子集;最后基于最优特征子集用SVM对待测样本进行分类识别。实验结果显明,在识别周界入侵信号时,基于PSO-GWO-SVM算法的分类器获得了96.86%的准确率、95.82%的灵敏度(SE)和96.31%的特异性。与传统的信号识别方法相比,具有更优异的识别精度、适应性和时效性。 相似文献
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为了准确识别电网中的低零值绝缘子,提高劣化绝缘子诊断的准确率,提出了一种使用灰狼算法优化(grey wolf optimizer, GWO)与二进制支持向量机(support vector machine, SVM)分类器相结合的模型,对红外图像中的低零值绝缘子进行自动检测。首先对绝缘子红外图像进行增强,利用Ostu算法对红外图像进行分割,并对得到的二值图像进行倾斜角度矫正和切割,提取绝缘子串的有效区域,然后将图像特征用于向量机的分类识别。实验结果表明,灰狼算法优化支持向量机比常用的网格搜索算法(grid search, GS)、粒子群优化算法(particle swarm optimization, PSO)等得到的分类模型能更准确、有效地对低零值绝缘子进行识别,且准确率更高。 相似文献
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针对单模态的心电信号(ECG)或光电容积脉搏波信号(PPG)识别技术中存在的精度不高,未考虑类内相关性等问题,该文提出基于判别相关分析法(DCA)对ECG与PPG组合特征矩阵进行特征层融合以及对K-最近邻(KNN)和支持向量机(SVM)分类器在决策层融合的识别方法。实验结果表明,使用融合特征(ECG-PPG)与融合分类器(KNN-SVM)的方法对23名受试者进行分类识别的准确率可以达到98.2%,识别精度在常规环境下优于单模态识别。为多模生物特征身份识别提供了一种有效模型。 相似文献