共查询到20条相似文献,搜索用时 468 毫秒
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支持向量机最优模型选择的研究 总被引:18,自引:0,他引:18
通过对核矩阵的研究,利用核矩阵的对称正定性,采用核校准的方法提出了一种SVM最优模型选择的算法——OMSA算法.利用训练样本不通过SVM标准训练和测试过程而寻求最优的核参数和相应的最优学习模型,弥补了传统SVM在模型选择上经验性强和计算量大的不足.采用该算法在UCI标准数据集和FERET标准人脸库上进行了实验,结果表明,通过该算法找到的核参数以及相应的核矩阵是最优的,得到的SVM分类器的错误率最小.该算法为SVM最优模型选择提供了一种可行的方法,同时对其他基于核的学习方法也具有一定的参考价值. 相似文献
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介绍了一种基于遗传算法辨识线性离散系统参数的方法,为了提高算法的优化能力,将基率遗传算法和梯度法结合起来。仿真结果表明.改进的遗传算法辨识系统参数收敛到全局最优.且速度快,精度高.鲁棒性强。 相似文献
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基于混沌优化算法的支持向量机参数选取方法 总被引:31,自引:0,他引:31
支持向量机(SVM)的参数取值决定了其学习性能和泛化能力.对此,将SVM参数的选取看作参数的组合优化,建立组合优化的目标函数,采用变尺度混沌优化算法来搜索最优目标函数值.混沌优化算法是一种全局搜索方法,在选取SVM参数时,不必考虑模型的复杂度和变量维数.仿真表明,混沌优化算法是选取SVM参数的有效方法,应用到函数逼近时具有优良的性能. 相似文献
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提出一种新的技术,它自适应地选取正则化参数以取得较理想的恢复效果.利用小波变换,分析正则化算子和正则化参数对图象残差的各子频段能量的影响.在本文条件下,我们论证正则化算子取拉普拉斯算子比取恒等算子恢复性能好,并且预测噪声能量.实验结果表明本文提出的方法不需要知道噪声能量,也能够自适应地确定正则化参数并且恢复性能比传统的方法好,恢复效果非常接近最优恢复. 相似文献
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多目标遗传算法(MOGA)是求解多目标优化问题的有效工具,因而在求解实际问题中得到越来越广泛的应用.PCA是一种基于二阶统计的最小均方误差意义上的最优维数压缩技术,PCA方法所抽取特征的各分量之间是统计不相关的.在人脸识别的实际应用中,将多目标遗传算法引入到PCA所生成的特征空间的优化中,提出基于双重特征空间的人脸识别算法.通过对剑桥ORL库实验表明,该方法与传统的PCA相比,识别率得到明显提高. 相似文献
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化工过程的多目标优化综合问题可归结为多目标混合整数非线性规划(MOMINLP)模型的求解,求解方法主要有数学规划法和多目标进化算法。以多目标遗传算法(MOGA)为代表的进化算法被认为是特别适合求解此类问题。遗传算法大多用于单目标问题的优化,近十几年来将遗传算法应用到多目标优化的研究得到了很大的发展。本文对多目标遗传算法的一些重要概念、发展历程进行了回顾。针对化工过程的模型特点,对MOGA在过程综合中的应用研究进行了讨论,并认为混合遗传算法应是求解此类问题的有效算法。 相似文献
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Kun-Hong Liu Author Vitae Bo Li Author Vitae Author Vitae Ji-Xiang Du Author Vitae 《Pattern recognition》2009,42(7):1274-1283
Independent component analysis (ICA) has been widely used to tackle the microarray dataset classification problem, but there still exists an unsolved problem that the independent component (IC) sets may not be reproducible after different ICA transformations. Inspired by the idea of ensemble feature selection, we design an ICA based ensemble learning system to fully utilize the difference among different IC sets. In this system, some IC sets are generated by different ICA transformations firstly. A multi-objective genetic algorithm (MOGA) is designed to select different biologically significant IC subsets from these IC sets, which are then applied to build base classifiers. Three schemes are used to fuse these base classifiers. The first fusion scheme is to combine all individuals in the final generation of the MOGA. In addition, in the evolution, we design a global-recording technique to record the best IC subsets of each IC set in a global-recording list. Then the IC subsets in the list are deployed to build base classifier so as to implement the second fusion scheme. Furthermore, by pruning about half of less accurate base classifiers obtained by the second scheme, a compact and more accurate ensemble system is built, which is regarded as the third fusion scheme. Three microarray datasets are used to test the ensemble systems, and the corresponding results demonstrate that these ensemble schemes can further improve the performance of the ICA based classification model, and the third fusion scheme leads to the most accurate ensemble system with the smallest ensemble size. 相似文献
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Obtaining a fullest possible representation of solutions to a multiobjective optimization problem has been a major concern in Multi-Objective Genetic Algorithms (MOGAs). This is because a MOGA, due to its very nature, can only produce a discrete representation of Pareto solutions to a multiobjective optimization problem that usually tend to group into clusters. This paper presents a new MOGA, one that aims at obtaining the Pareto solutions with maximum possible coverage and uniformity along the Pareto frontier. The new method, called an Entropy-based MOGA (or E-MOGA), is based on an application of concepts from the statistical theory of gases to a baseline MOGA. Two demonstration examples, the design of a two-bar truss and a speed reducer, are used to demonstrate the effectiveness of E-MOGA in comparison to the baseline MOGA. 相似文献
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为获得精确可靠的航空发动机外部管道结构动力学模型,采用将Kriging模型与多目标遗传算法(MOGA)相结合的模型修正方法进行有限元模型修正.首先进行管道模型的模态试验和有限元建模,分别获得模态参数的试验值和有限元分析值;然后在合理的参数选取和试验设计(DOE)的基础上,拟合得到Kriging模型;最后基于Kriging模型采用多目标遗传算法进行有限元模型修正,并对比了不同修正方法的精度和修正效果.结果表明:采用Kriging模型进行有限元模型修正可以有效提升修正效果,获得更为准确的有限元模型;对于航空发动机管道系统,基于Kriging模型的模型修正方法相较于基于灵敏度分析的模型修正方法具有更高的修正效率和修正精度. 相似文献
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针对单目标遗传算法设计优化高阶Σ-Δ微机电系统(Σ-ΔMEMS)加速度计时易出现的稳定性问题,提出了基于多目标遗传算法的MEMS加速度计环路滤波器优化设计方法.对三阶非限定性Σ-ΔMEMS加速度计系统,采用多目标遗传算法,将∞—范数和信噪比作为设计目标对其环路滤波器参数进行优化设计.结果表明:相比只针对信噪比进行优化的传统单目标遗传算法,多目标遗传算法在确保高信噪比的同时,提高了系统的相位裕度,使得最大稳定输入信号范围增幅超过1倍,增强了系统对MEMS敏感元件工艺误差的鲁棒性. 相似文献
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Improving multi-objective genetic algorithms with adaptive design of experiments and online metamodeling 总被引:1,自引:1,他引:0
G. Li M. Li S. Azarm S. Al Hashimi T. Al Ameri N. Al Qasas 《Structural and Multidisciplinary Optimization》2009,37(5):447-461
Applications of multi-objective genetic algorithms (MOGAs) in engineering optimization problems often require numerous function
calls. One way to reduce the number of function calls is to use an approximation in lieu of function calls. An approximation
involves two steps: design of experiments (DOE) and metamodeling. This paper presents a new approach where both DOE and metamodeling
are integrated with a MOGA. In particular, the DOE method reduces the number of generations in a MOGA, while the metamodeling
reduces the number of function calls in each generation. In the present approach, the DOE locates a subset of design points
that is estimated to better sample the design space, while the metamodeling assists in estimating the fitness of design points.
Several numerical and engineering examples are used to demonstrate the applicability of this new approach. The results from
these examples show that the proposed improved approach requires significantly fewer function calls and obtains similar solutions
compared to a conventional MOGA and a recently developed metamodeling-assisted MOGA. 相似文献
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Kourosh Behzadian Zoran Kapelan Dragan Savic Abdollah Ardeshir 《Environmental Modelling & Software》2009,24(4):530-541
This paper presents a novel multi-objective genetic algorithm (MOGA) based on the NSGA-II algorithm, which uses metamodels to determine optimal sampling locations for installing pressure loggers in a water distribution system (WDS) when parameter uncertainty is considered. The new algorithm combines the multi-objective genetic algorithm with adaptive neural networks (MOGA–ANN) to locate pressure loggers. The purpose of pressure logger installation is to collect data for hydraulic model calibration. Sampling design is formulated as a two-objective optimization problem in this study. The objectives are to maximize the calibrated model accuracy and to minimize the number of sampling devices as a surrogate of sampling design cost. Calibrated model accuracy is defined as the average of normalized traces of model prediction covariance matrices, each of which is constructed from a randomly generated sampling set of calibration parameter values. This method of calculating model accuracy is called the ‘full’ fitness model. Within the genetic algorithm search process, the full fitness model is progressively replaced with the periodically (re)trained adaptive neural network metamodel where (re)training is done using the data collected by calling the full model. The methodology was first tested on a hypothetical (benchmark) problem to configure the setting requirement. Then the model was applied to a real case study. The results show that significant computational savings can be achieved by using the MOGA–ANN when compared to the approach where MOGA is linked to the full fitness model. When applied to the real case study, optimal solutions identified by MOGA–ANN are obtained 25 times faster than those identified by the full model without significant decrease in the accuracy of the final solution. 相似文献
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提出一种改进的显模型跟踪??∞回路成形控制方法, 利用??∞ 回路成形算法补偿显模型跟踪算法中前馈模型逆的不确定性. 针对??∞ 回路成形控制算法中权重函数选取的盲目性, 利用多目标遗传算法, 结合改进的小生境淘汰技术对权重函数进行寻优, 以提高设计效率和准确性. 基于所提出的方法设计直升机的内回路显模型跟踪??∞ 回路成形姿态控制系统, 能够提高系统的鲁棒性.
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