共查询到20条相似文献,搜索用时 125 毫秒
1.
研究利用最小二乘支持向量机(LS-SVM)预测变参数混沌时间序列。支持向量机方法是基于结构风险最小化原理导出的,最小二乘支持向量机是一种在二次损失函数下采用等式约束求解问题的支持向量机,保留支持向量机优点的同时计算量大大减少。变参数混沌时间序列预测是典型的小样本学习问题,由于参数的慢变导致系统的动力学特性不断发生变化,全局建模预测方法很难适用,必须在线实时预测。为了快速跟踪预测变参数混沌系统的时间序列,研究了利用一种简化的最小二乘支持向量机在线递推算法进行预测。最后对典型变参数混沌时间序列的预测实验结果表明了该方法的有效性。 相似文献
2.
研究混沌时间序列预测问题,混沌时间序列是一种特殊时间序列,具有高度非线性、混沌性和时变性,传统预测方法精度低。为了提高混沌时间序列预测的精度,提出一种采用小波支持向量机的混沌时间序列预测算法。首先采用相空重构对混沌时间序列进行重构,捕捉时间序列变化轨迹,然后采用小波支持向量机对非线性、混沌性变化规律进行预测,最后采用Lorenz混沌时间序列进行仿真。结果表明,相对于传统预测方法,小波支持向量机提高了混沌时间序列预测精度,降低预测误差,为混沌时间序列提供了一种有效的准确预测途径。 相似文献
3.
研究混沌时间序列预测准确性问题,由于混沌时间序列具有混沌性和非线性,传统时间序列预测方法不能准确将混沌时间序列变化规律计算出来,导致预测精度低.为了提高混沌时间序列预测的精度,提出一种改进支持向量机的混沌时间序列预测方法(PSO-LSSVM).PSO-LSSVM采用相空间重构对混沌时间序列进行重构,去除其混沌性,用支持向量机对非线性进行预测,并采用粒子群算法对支持向量机参数进行优化,对经典混沌时间序列Mackey-Glass最优模型进行仿真测试.仿真结果表明,PSO-LSSVM加快了预测速度,提高了预测精度,在混沌时间序列预测中具有很好的应用价值. 相似文献
4.
5.
6.
研究网络流量准确预测问题,由于网络流量存在混沌性、非线性、自相似性的特点,导致当前预测方法的网络流量预测精度差。为提高网络流量预测精度,提出了小波支持向量机的网络流量预测模型。首先根据混沌动力系统对网络流量时间序列进行相空间重构,然后采用支持向量机对其进行建模,并利用小波核函数提高支持向量机的泛化能力。进行仿真的结果表明,支持向量机方法提高了网络流量的预测精度,减少训练时间,泛化能力更优。仿真结果说明,小波支持向量机具有更强的实用价值。 相似文献
7.
介绍了相空间重构和基于支持向量机的时间序列预测建模技术,提出了基于小波和支持向量机的复杂时间序列预测方法,利用小波对复杂时间序列进行多尺度分解,对重构后的近似序列和细节序列分别利用支持向量机进行回归预测并将结果融合。对股票数据进行预测,试验结果表明该方法预测精度高于单尺度支持向量机和神经网络预测方法,可用于复杂非平稳时间序列的预测。 相似文献
8.
最小二乘支持向量机在混沌时间序列中的应用 总被引:1,自引:0,他引:1
针对混沌时间序列存在噪声和不稳定性问题,混沌时间序列预测是当前的一个热点问题.根据当前预测方法预测精度低的难题,提出一种优化的混沌时间序列预测方法(GA_LSSVM).GA_LSSVM首先采用相空间重构对时间序列样本进行重构,采用遗传算法对最小二乘支持向量机参数进行优化,得到混沌时间序列的最优预测模型,最后以经典时间序列Lorenz数据对最优模型进行仿真测试和分析.实验结果表明,GA_LSSVM比神经网络的预测精度高.GA_LSSVM方法是一种可行的、有效的混沌时间序列预测算法,在混沌时间序列具有广泛的应用前景,可为应用提供科学依据. 相似文献
9.
为提高网络流量的预测精度,提出一种基于混沌理论和最小二乘支持向量机相结合的网络流量预测方法。采用相空间重构对网络流量时间序列进行重构,恢复网络流量的演化轨迹,采用非线性预测能力强的最小二乘支持向量机对网络流量时间序列进行训练建模,采用混沌粒子群算法对最小二乘支持向量机参数进行优化,从而获得最优网络流量预测模型。用实际网络流量数据对该算法有效性进行验证,结果表明该方法能够很好刻画网络流量的变化趋势,提高了网络流量的预测精度,预测性能优于传统的预测方法。 相似文献
10.
11.
基于SVR的混沌时间序列预测 总被引:11,自引:0,他引:11
支持向量机是一种基于统计学习理论的新颖的机器学习方法,由于其出色的学习性能,该技术已成为当前国际机器学习界的研究热点。这种方法已广泛用于解决分类和回归问题。论文介绍了支持向量回归算法的各种版本,同时将它们应用到混沌时间序列预测中,并且比较了它们的预测性能,为实际应用合理选择模型提供一定的依据。 相似文献
12.
An unbiased LSSVM model for classification and regression 总被引:1,自引:0,他引:1
Hong-Qiao Wang Fu-Chun Sun Yan-Ning Cai Lin-Ge Ding Ning Chen 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2010,14(2):171-180
Aiming at the common support vector machine’s biased disadvantage and computational complexity, an unbiased least squares
support vector machine (LSSVM) model is proposed in this paper. The model eliminates the bias item of LSSVM by improving the
form of structure risk, then the unbiased least squares support vector classifier and the unbiased least squares support vector
regression are deduced. Based on this model, we design a new learning algorithm using Cholesky factorization according to
the characteristic of kernel function matrix, in this way the calculation of Lagrangian multipliers is greatly simplified.
Several experiments on diffenert datasets are carried out, including the common datasets classification, synthetic aperture
radar image automatic target recognition and chaotic time series prediction. The experimental results of correct recognition
rate and the fitting precision testify that the unbiased LSSVM model has good universal ability and fitting accuracy, better
generalization capability and stability, and have a great improvement in learning speed. 相似文献
13.
14.
混沌的特性决定了混沌系统很难长期预测,支持向量机有强大的学习能力,根据相空间重构理论用支持向量机建立预测模型对混沌时间序列进行短期预测。预测输出构建混沌吸引子来定性评价预测模型性能,同时与BP神经网络RBF神经网络构建的预测模型比较,计算预测模型的均方根误差定量地评价模型的性能。仿真结果表明,该方法具有较高的预测精度和泛化能力。 相似文献
15.
LS—SVM在混沌时间序列预测中的应用 总被引:9,自引:0,他引:9
支持向量机是一种基于统计学习理论的新颖的机器学习方法,该方法已广泛用于解决分类和回归问题。文中将最小二乘支持向量机算法应用于混沌时间序列预测中,并同BP网络及RBF网络的预测结果进行了比较分析。仿真实验表明,该方法具有很好的泛化能力和一定的噪声容忍能力。 相似文献
16.
支持向量回归中的预测信任度 总被引:3,自引:1,他引:3
Support vector machine (SVM)has been widely applied to classification and regression problems, but it suf-fers from some important limitations, one of the most significant being that it makes point predictions rather thangenerating probability output. A notion of predicting credibility is proposed in support vector regression machine based on the problem, which can make predicting value have a definite measure, and then relationship between pre-dicting credibility and noise is discussed. Finally, an example of predicting chaotic time series shows the rationality of the definition. 相似文献
17.
基于无约束优化的非线性支持向量回归 总被引:2,自引:0,他引:2
提出利用牛顿法以及共轭梯度法解决非线性支持向量回归学习问题,不仅可以加速模型选择的过程,而且能够提高训练速度.将该方法应用于煤气炉数据集建模以及Mackey-Glass混沌时间序列预测,仿真结果表明了该方法的有效性. 相似文献
18.
在混沌时间序列研究中,相空间重构和预测模型参数优化是影响预测性能的关键步骤,利用两者之间的相互联系来提高混沌时间序列预测模型的整体性能,提出一种基于遗传算法的混沌时间序列参数同步优化方法。同步优化方法将相空间重构和最小二乘支持向量机参数作为遗传算法的染色体,预测精度作为遗传算法的适应度函数值,通过遗传算法对参数同步优化问题进行求解。通过混沌时间数据对同步优化方法进行了验证性实验。实验结果表明,相对于单独参数优化方法,同步优化方法不仅提高了混沌时间序列的预测精度,同时降低了计算时间的复杂度。 相似文献
19.
Online chaotic time series prediction using unbiased composite kernel machine via Cholesky factorization 总被引:1,自引:1,他引:0
Hongqiao Wang Fuchun Sun Yanning Cai Zongtao Zhao 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2010,14(9):931-944
The kernel method has proved to be an effective machine learning tool in many fields. Support vector machines with various
kernel functions may have different performances, as the kernels belong to two different types, the local kernels and the
global kernels. So the composite kernel, which can bring more stable results and good precision in classification and regression,
is an inevitable choice. To reduce the computational complexity of the kernel machine’s online modeling, an unbiased least
squares support vector regression model with composite kernel is proposed. The bias item of LSSVR is eliminated by improving
the form of structure risk in this model, and then the calculating method of the regression coefficients is greatly simplified.
Simultaneously, through introducing the composite kernel to the LSSVM, the model can easily adapt to the irregular variation
of the chaotic time series. Considering the real-time performance, an online learning algorithm based on Cholesky factorization
is designed according to the characteristic of extended kernel function matrix. Experimental results indicate that the unbiased
composite kernel LSSVR is effective and suitable for online time series with both the steep variations and the smooth variations,
as it can well track the dynamic character of the series with good prediction precisions, better generalization and stability.
The algorithm can also save much computation time comparing to those methods using matrix inversion, although there is a little
more loss in time than that with the usage of single kernels. 相似文献
20.
In this paper, an integrated model based on efficient extreme learning machine (EELM) and differential evolution (DE) is proposed to predict chaotic time series. In the proposed model, a novel learning algorithm called EELM is presented and used to model the chaotic time series. The EELM inherits the basic idea of extreme learning machine (ELM) in training single hidden layer feedforward networks, but replaces the commonly used singular value decomposition with a reduced complete orthogonal decomposition to calculate the output weights, which can achieve a much faster learning speed than ELM. Moreover, in order to obtain a more accurate and more stable prediction performance for chaotic time series prediction, this model abandons the traditional two-stage modeling approach and adopts an integrated parameter selection strategy which employs a modified DE algorithm to optimize the phase space reconstruction parameters of chaotic time series and the model parameter of EELM simultaneously based on a hybrid validation criterion. Experimental results show that the proposed integrated prediction model can not only provide stable prediction performances with high efficiency but also achieve much more accurate prediction results than its counterparts for chaotic time series prediction. 相似文献