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基于稀疏最小二乘支持向量回归的非线性自适应波束形成 总被引:1,自引:0,他引:1
该文基于最小二乘支持向量回归(LS-SVR)模型提出一种非线性自适应波束形成算法,以提高模型失配、小样本数、复杂多干扰等情况下的自适应波束形成器的鲁棒性。推导了高维矩阵逆矩阵求解的递推快速算法,实现了回归参数的实时求解。采用奇异性准则实时寻找输入样本集的具有较小信息冗余度的子集,并在该子集上完成波束形成计算,使得LS-SVR波束形成的求解得以稀疏化,提高了学习效率,降低了计算复杂度与系统存储空间需求。对比仿真结果验证了所提算法的正确性和有效性。 相似文献
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基于最小二乘支持向量机回归算法,本文在前期工作的基础上进行了扩展,提出了更加详尽的自适应迭代最小二乘支持向量机回归算法. 与标准的LSSVR相比,本文提出的算法在学习新样本的时候利用了已有的学习结果,可以快速获得新的学习机. 模拟结果表明,自适应迭代最小二乘支持向量机回归算法能够自适应地确定支持向量的数目,保留了QP方法在训练SVM时支持向量的稀疏性,在相近的回归精度下,该算法极大地提高了标准LSSVR学习的速度. 相似文献
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本文提出一种适用于任意未知统计特性的代数拖尾冲击噪声(包含所有对称α稳定分布噪声)环境下的波束形成算法.算法利用输出信号和参考信号之间"几何功率"误差的最小化来求解最优权向量."几何功率"误差定义成误差信号的对数矩的形式.我们采用迭代复加权最小二乘估计来求解最小"几何功率"误差波束形成权向量.与基于最小分数低阶误差波束形成算法相比,最小"几何功率"误差波束形成算法计算更为简单;不需要噪声特征指数的先验信息或估计;适用于更广的冲击噪声环境;具有更小的估计误差.计算机仿真验证了算法的有效性. 相似文献
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为提高活性炭含量检测的效率与精度,基于微波谐振技术设计了一种活性炭滤棒微波幅值变化信号采集装置,并将高斯滤波和惩罚最小二乘算法相结合对微波幅值变化信号进行降噪和基线扣除处理。首先,比较了不同高斯窗口长度的滤波效果,选用非对称最小二乘法、自适应迭代重加权惩罚最小二乘法、非对称重加权惩罚最小二乘法和多约束重加权惩罚最小二乘法等4种处理方法对微波幅值变化信号进行基线校正,再求出基线校正后微波幅值变化信号的峰高、峰面积与半峰全宽,然后比较了基于支持向量回归机、偏最小二乘算法与反向传播神经网络建立的模型的预测结果。结果显示,活性炭质量的最佳模型为“峰面积-活性炭质量”,模型决定系数为0.9924,平均绝对误差为0.7979 mg,相对标准偏差为1.4962%。活性炭质量重复性检测最大标准差为1.85 mg,活性炭质量检测的最小绝对偏差为0.03 mg,活性炭质量检测最小相对偏差为0.05%。该方法为烟用活性炭滤棒中活性炭的定量分析提供了一种快速有效的方法。 相似文献
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针对传统的光纤光栅电压传感器非线性校正算法具 有运行速度慢,拟合精度不高的缺陷。在研究了大量国内外文献过后,本文为了解决一些传 统非线性校正方法在光栅光纤传感器校正中的不足,在此提出了一种基于蚁群算法优化的分 段支持向量机回归的 校正算法。由于传统的蚁群算法在信号处理中搜索速度不理想,最小二乘支持向量机回归算 法精度不高,所以此算法是结合了蚁群 算法搜索最小二乘支持向量机回归最佳参数原理的基础上将样本空间按照数据分布情况进行 分段回归,以此减少算法运行时间。首 先通过蚁群算法优化各个支持向量机参数,然后通过分段回归得到传感器完整的特性,曲线 拟合精度为99.97%。此算法克服了传统 支持向量机回归算法中局部最优解的问题,具有较好的全局收敛效果。 相似文献
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The problem to improve the performance of resisting geometric attacks in digital watermarking is addressed in this paper.Based on the optimized support vector regression(SVR),a zero-bit watermarking algorithm is presented.The proposed algorithm encrypts the watermarking image by using composite chaos with large key space and capacity against prediction,which can strengthen the safety of the proposed algorithm.By using the relationship between Tchebichef moment invariants of detected image and watermarking characteristics,the SVR training model optimized by composite chaos enhances the ability of resisting geometric attacks.Performance analysis and simulations demonstrate that the proposed algorithm herein possesses better security and stronger robustness than some similar methods. 相似文献
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Jie Li Jia Yan Dexiang Deng Wenxuan Shi Songfeng Deng 《Signal, Image and Video Processing》2017,11(6):985-992
The aim of research on the no-reference image quality assessment problem is to design models that can predict the quality of distorted images consistently with human visual perception. Due to the little prior knowledge of the images, it is still a difficult problem. This paper proposes a computational algorithm based on hybrid model to automatically extract vision perception features from raw image patches. Convolutional neural network (CNN) and support vector regression (SVR) are combined for this purpose. In the hybrid model, the CNN is trained as an efficient feature extractor, and the SVR performs as the regression operator. Extensive experiments demonstrate very competitive quality prediction performance of the proposed method. 相似文献
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《IEEE transactions on information technology in biomedicine》2009,13(1):57-66
Accurate estimation of fetal weight before delivery is of great benefit to limit the potential complication associated with the low-birth-weight infants. Although the regression analysis has been used as a daily clinical means to estimate the fetal weight on the basis of ultrasound measurements, it still lacks enough accuracy for low-birth-weight fetuses. The ineffectiveness is mainly due to the large inter- or intraobserver variability in measurements and the inappropriateness of the regression analysis. A novel method based on the support vector regression (SVR) is proposed to improve the weight estimation accuracy for fetuses of less than 2500 g. Here, fuzzy logic is introduced into SVR (termed FSVR) to limit the contribution of inaccurate training data to the model establishment, and thus, to enhance the robustness of FSVR to noisy data. To guarantee the generalization performance of the FSVR model, the nondominated sorting genetic algorithm (NSGA) is utilized to obtain the optimal parameters for the FSVR, which is referred to as the evolutionary fuzzy support vector regression (EFSVR) model. Compared with regression formulas, back-propagation neural network, and SVR, EFSVR achieves the lowest mean absolute percent error (6.6%) and the highest correlation coefficient (0.902) between the estimated fetal weight and the actual birth weight. The EFSVR model produces significant improvement (1.9%-4.2%) on the accuracy of fetal weight estimation over several widely used formulas. Experiments show the potential of EFSVR in clinical prenatal care. 相似文献
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Image superresolution using support vector regression. 总被引:6,自引:0,他引:6
A thorough investigation of the application of support vector regression (SVR) to the superresolution problem is conducted through various frameworks. Prior to the study, the SVR problem is enhanced by finding the optimal kernel. This is done by formulating the kernel learning problem in SVR form as a convex optimization problem, specifically a semi-definite programming (SDP) problem. An additional constraint is added to reduce the SDP to a quadratically constrained quadratic programming (QCQP) problem. After this optimization, investigation of the relevancy of SVR to superresolution proceeds with the possibility of using a single and general support vector regression for all image content, and the results are impressive for small training sets. This idea is improved upon by observing structural properties in the discrete cosine transform (DCT) domain to aid in learning the regression. Further improvement involves a combination of classification and SVR-based techniques, extending works in resolution synthesis. This method, termed kernel resolution synthesis, uses specific regressors for isolated image content to describe the domain through a partitioned look of the vector space, thereby yielding good results. 相似文献
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用机器学习方法进行电力负荷宏观预测 总被引:1,自引:1,他引:0
刘遵雄 《微电子学与计算机》2004,21(5):160-162
分析了电力负荷宏观预测的模型和相关技术,引入支持向量回归方法(SVR)解决问题,并通过计算实例,比较分析了SVR与神经网络方法用于预测的效果,提出SVR广阔应用前景。 相似文献
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Camps-Valls G. Soria-Olivas E. Perez-Ruixo J.J. Perez-Cruz F. Figueiras-Vidal A.R. Artes-Rodriguez A. 《Electronics letters》2002,38(12):568-570
A combined strategy of clustering and support vector regression (SVR) methods is proposed to predict Cyclosporine A (CyA) concentration in renal transplant recipients. Clustering combats the high variability and non-stationarity of the time series and reports knowledge gain in the problem. The SVR outperforms other classical neural networks 相似文献