萤火虫算法优化支持向量机室内定位研究

针对室内定位指纹库匹配冗余信息多造成定位浮动大,且数据库中样本数过多定位时效性差等问题,提出一种基于萤火虫算法FA优化支持向量机SVM的室内定位算法FA-SVM。利用奇异谱分析SSA预处理数据去除噪声,通过萤火虫算法优化支持向量机参数,建立室内定位回归模型。实验结果表明,相对于目前其它室内定位算法,FA-SVM算法收敛速度快,提高了室内定位精度和稳定性。     

利用支持向量回归机设计IDS的检测算法

为提高网络入侵检测系统中检测算法的分类精度,降低训练样本及学习时间,提出一种新的基于支持向量回归机的检测算法。算法首先归一化处理训练样本数据,然后精确调节松弛惩罚因子,最后使用KDD CUP 1999数据集进行仿真实验,结果表明本算法可以提高入侵检测的准确性和有效性,并能够降低误报率。     

Novel approach for forecasting the blast-induced AOp using a hybrid fuzzy system and firefly algorithm

Air overpressure (AOp) produced by blasting is one of the environmental hazards of mining operations. Accordingly, the accurate prediction of AOp is very important, and this issue requires the application of appropriate prediction models. With this in view, this paper aims to propose a new data-driven model in the prediction of AOp using a hybrid model of fuzzy system (FS) and firefly algorithm (FA). This combination is abbreviated as FS-FA model. The used data-sets in the proposed FS-FA model were arranged in a format of three input parameters. In total, 86 sets of the mentioned parameters were prepared. To avoid over-fitting, the data-sets were divided into two parts of training (80%) and test sets (20%). Three quantitative standard statistical performance evaluation measures, variance account for (VAF), coefficient correlation (R2) and root mean squared error (RMSE), were used to check the accuracy of the FS-FA model. According to the results, the R2 and RMSE values obtained from the proposed FS-FA model were equal to 0.977 and 1.241 (for testing phase), respectively, which clearly demonstrate the merits of the proposed FS-FA model. In other words, the obtained R2 and RMSE show that FS-FA model has high prediction level in the modeling of blast-induced AOp.     

Support vector regression with chaos-based firefly algorithm for stock market price forecasting

Due to the inherent non-linearity and non-stationary characteristics of financial stock market price time series, conventional modeling techniques such as the Box–Jenkins autoregressive integrated moving average (ARIMA) are not adequate for stock market price forecasting. In this paper, a forecasting model based on chaotic mapping, firefly algorithm, and support vector regression (SVR) is proposed to predict stock market price. The forecasting model has three stages. In the first stage, a delay coordinate embedding method is used to reconstruct unseen phase space dynamics. In the second stage, a chaotic firefly algorithm is employed to optimize SVR hyperparameters. Finally in the third stage, the optimized SVR is used to forecast stock market price. The significance of the proposed algorithm is 3-fold. First, it integrates both chaos theory and the firefly algorithm to optimize SVR hyperparameters, whereas previous studies employ a genetic algorithm (GA) to optimize these parameters. Second, it uses a delay coordinate embedding method to reconstruct phase space dynamics. Third, it has high prediction accuracy due to its implementation of structural risk minimization (SRM). To show the applicability and superiority of the proposed algorithm, we selected the three most challenging stock market time series data from NASDAQ historical quotes, namely Intel, National Bank shares and Microsoft daily closed (last) stock price, and applied the proposed algorithm to these data. Compared with genetic algorithm-based SVR (SVR-GA), chaotic genetic algorithm-based SVR (SVR-CGA), firefly-based SVR (SVR-FA), artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS), the proposed model performs best based on two error measures, namely mean squared error (MSE) and mean absolute percent error (MAPE).     

Simulation and prediction of hydrological processes based on firefly algorithm with deep learning and support vector for regression

ABSTRACT

Hydrological processes are hard to accurately simulate and predict because of various natural and human influences. In order to improve the simulation and prediction accuracy of the hydrological process, the firefly algorithm with deep learning (DLFA) was used in this study to optimise the parameters of support vector for regression (SVR) automatically, and a prediction model was established based on DLFA and SVR. The hydrological process of Huangfuchuan in Fugu County, Shanxi Province was taken as the research object to verify the performance of the prediction model, and the results were compared with those by the other six prediction models. The experimental results showed that the proposed prediction model achieved improved prediction performance compared with the other six models.     

基于套索算法和改进正余弦优化支持向量回归的目标威胁估计

准确估计空中目标的威胁值对于防空作战指挥决策具有重要的参考意义.针对空中目标特征繁杂易造成模型过拟合、正余弦算法易早熟以及陷入局部最优的不足,首先,通过套索算法(least absolute shrinkage and selection operator, LASSO)去除目标的冗余特征;然后,采用佳点集初始化种群、非线性振幅调整因子、随机惯性权重、自适应终点权重以及最优邻域高斯扰动等策略对正余弦算法(sine cosine algorithm, SCA)进行改进;最后,使用改进的正余弦算法对支持向量回归(support vector regression, SVR)模型进行优化,构建基于套索算法和改进正余弦优化支持向量回归的目标威胁估计模型.对比实验结果表明,改进后的正余弦算法加强了全局搜索能力和局部收敛速度,得到的目标威胁估计模型具有较高的准确度和稳定性,能够为防空作战指挥决策提供科学的参考依据.     

一种支持向量逐步回归机算法研究

支持向量机是解决非线性问题的重要工具,对多元线性回归模型和支持向量机的原始形式进行比较,拟定从样本子集的多元线性回归模型出发,逐步搜索支持向量,提出了一种建立支持向量回归机的快速算法,以降低核矩阵的规模从而降低解凸二次规划的复杂度;最后,分析了该算法的复杂度,并提供了一个算例。     

基于支持向量回归的单步多分类算法

分析了利用支持向量回归求解多分类问题的思想,提出了一种基于局部密度比权重设置模型的加权最小二乘支持向量回归模型来单步求解多分类问题：该方法先分别对类样本中每类样本利用局部密度比权重设置模型求出每个样本的权重隶属因子,然后运用加权最小二乘支持向量回归算法对所有样本进行训练,获得回归分类器。为验证算法的有效性,对UCI三个标准数据集以及一个随机生成的数据集进行实验,对比了多种单步求解多分类问题的算法,结果表明,提出的模型分类精度高,具有良好的鲁棒性和泛化性能。     

基于支持向量回归的行程时间预测算法

作为交通规划、运营和通行能力评估的重要指标,行程时间的预测对出行者的路线和时间点的选择,以及交通规划部门的信号控制策略有着重要的实际意义。对于高级交通诱导系统而言,行程时间预测是一项关键的研究内容。现有行程时间预测方法较少,且预测误差较大。为此,运用浮动车和微波雷达测速数据,提出了基于支持向量机解决行程时间预测的方法,并且与历史平均法进行了比较。在杭州市高架路线上的实验结果表明,所提方法的预测精度大幅度超过了历史平均法。     

拉格朗日支持向量回归的有限牛顿算法

拉格朗日支持向量回归是一种有效的快速回归算法,求解时需要对维数等于样本数加一的矩阵求逆,求解需要较多的迭代次数才能收敛。采用一种Armijo步长有限牛顿迭代算法求解拉格朗日支持向量回归的优化问题,只需有限次求解一组线性等式而不需要求解二次规划问题,该方法具有全局收敛和有限步终止的性质。在多个标准数据集上的实验验证了所提算法的有效性和快速性。     

Bayesian support vector regression using a unified loss function

In this paper, we use a unified loss function, called the soft insensitive loss function, for Bayesian support vector regression. We follow standard Gaussian processes for regression to set up the Bayesian framework, in which the unified loss function is used in the likelihood evaluation. Under this framework, the maximum a posteriori estimate of the function values corresponds to the solution of an extended support vector regression problem. The overall approach has the merits of support vector regression such as convex quadratic programming and sparsity in solution representation. It also has the advantages of Bayesian methods for model adaptation and error bars of its predictions. Experimental results on simulated and real-world data sets indicate that the approach works well even on large data sets.     

AADT prediction using support vector regression with data-dependent parameters

Traffic volume is a fundamental variable in several transportation engineering applications. For instance, in transportation planning, the annual average daily traffic (AADT) is a primary element that has to be estimated for the year of horizon of the analysis. The huge amounts of money to be invested in designed transportation systems are strongly associated with the traffic volumes expected in the system, which means that it is important that the AADT should be accurately predicted. In this paper, a modified version of a pattern recognition technique known as support vector machine for regression (SVR) to forecast AADT is presented. The proposed methodology computes the SVR prediction parameters based on the distribution of the training data. Therefore, the proposed method is called SVR with data-dependent parameters (SVR-DP). Using 20 years of AADT for both rural and urban roads in 25 counties in the state of Tennessee, the performance of the SVR-DP was compared with those of Holt exponential smoothing (Holt-ES) and of ordinary least-square linear regression (OLS-regression). SVR-DP performed better than both methods; although the Holt-ES also presented good results.     

Covariance matrix forecasting using support vector regression

Applied Intelligence - Support vector regression is a promising method for time-series prediction, as it has good generalisability and an overall stable behaviour. Recent studies have shown that it...     

多输出数据依赖核支持向量回归快速学习算法

针对基于递推下降法的多输出支持向量回归算法在模型参数拟合过程中收敛速度慢、预测精度低的情况,使用一种基于秩2校正规则且具有二阶收敛速度的修正拟牛顿算法（BFGS）进行多输出支持向量回归算法的模型参数拟合,同时为了保证模型迭代过程中的下降量和全局收敛性,应用非精确线性搜索技术确定步长因子。通过分析支持向量机（SVM）中核函数的几何结构,构造数据依赖核函数替代传统核函数,生成多输出数据依赖核支持向量回归模型。将模型与基于梯度下降法、修正牛顿法拟合的多输出支持向量回归模型进行对比。实验结果表明,在200个样本下该算法的迭代时间为72.98 s,修正牛顿法的迭代时间为116.34 s,递推下降法的迭代时间为2065.22 s。所提算法能够减少模型迭代时间,具有更快的收敛速度。     

Global convergence of SMO algorithm for support vector regression

Global convergence of the sequential minimal optimization (SMO) algorithm for support vector regression (SVR) is studied in this paper. Given l training samples, SVR is formulated as a convex quadratic programming (QP) problem with l pairs of variables. We prove that if two pairs of variables violating the optimality condition are chosen for update in each step and subproblems are solved in a certain way, then the SMO algorithm always stops within a finite number of iterations after finding an optimal solution. Also, efficient implementation techniques for the SMO algorithm are presented and compared experimentally with other SMO algorithms.     

一种ε可调的在线支持向量回归及其训练算法

标准支持向量回归问题中,噪声较大的时段将包含较多的支持向量。提出一种时间窗内?着可调的支持向量回归方法,根据各时间窗的支持向量的比例动态调整?着,能够处理噪声时变的回归问题。并给出一种?着调整时的在线训练算法,避免重复求解凸规划问题。实例表明该方法的泛化能力和拟合精度较标准支持向量回归为优。     

一种数据域描述的加权支持向量回归算法

针对支持向量回归中由于噪声和孤立点带来的过拟合问题,提出了一种基于支持向量数据域描述的加权系数函数模型,根据样本到特征空间最小包含超球球心的距离来确定其加权系数。将提出的加权系数模型用于加权支持向量回归中,一维数据集仿真表明,提出的模型可以有效减小回归误差,提高支持向量回归算法的抗噪声能力。     

改进引力搜索最小二乘支持向量机交通流预测

为了提高基于最小二乘支持向量机的交通流预测模型的精度,提出一种新的改进引力搜索算法（TCK-AGSA）对其进行参数寻优。首先,基于Tent映射改进Kbest函数,使算法具有跳出局部最优的机制;然后,引入全局最优引导策略,使粒子加速朝向最优解移动;接着,将进化度因子和聚合度因子引入速度更新权重系数,使算法具有较强的自适应能力。针对12个基准函数的仿真结果表明,TCK-AGSA的性能优于GSA及其改进算法。最后,建立基于TCK-AGSA寻优的最小二乘支持向量机模型,并选取2016年贵州省高速公路真实交通流数据进行预测实验,结果表明该模型具有更好的预测精度、鲁棒性和泛化能力。     

Predicting defect-prone software modules using support vector machines

Effective prediction of defect-prone software modules can enable software developers to focus quality assurance activities and allocate effort and resources more efficiently. Support vector machines (SVM) have been successfully applied for solving both classification and regression problems in many applications. This paper evaluates the capability of SVM in predicting defect-prone software modules and compares its prediction performance against eight statistical and machine learning models in the context of four NASA datasets. The results indicate that the prediction performance of SVM is generally better than, or at least, is competitive against the compared models.