A new support vector machine optimized by improved particle swarm optimization and its application

A new support vector machine （SVM） optimized by an improved particle swarm optimization （PSO） combined with simulated annealing algorithm （SA） was proposed. By incorporating with the simulated annealing method, the global searching capacity of the particle swarm optimization（SAPSO） was enchanced, and the searching capacity of the particle swarm optimization was studied. Then, the improyed particle swarm optimization algorithm was used to optimize the parameters of SVM （c,σ and ε）. Based on the operational data provided by a regional power grid in north China, the method was used in the actual short term load forecasting. The results show that compared to the PSO-SVM and the traditional SVM, the average time of the proposed method in the experimental process reduces by 11.6 s and 31.1 s, and the precision of the proposed method increases by 1.24% and 3.18%, respectively. So, the improved method is better than the PSO-SVM and the traditional SVM.     

A PSO-SVM Model for Short-Term Travel Time Prediction Based on Bluetooth Technology

The accurate prediction of travel time along roadway provides valuable traffic information for travelers and traffic managers. Aiming at short?term travel time forecasting on urban arterials, a predict...     

基于PSO-SVM的煤与瓦斯突出强度预测模型

为有效预测煤与瓦斯的突出强度,分析了煤与瓦斯突出的主要影响因素,建立了基于粒子群优化支持向量机方法(PSO-SVM)的煤与瓦斯突出强度预测模型,通过实例对该模型的预测效果进行检验,同时还分别采用了BP神经网络(BP-NN)和支持向量机方法(SVM)对该实例进行了预测,进而对这3种方法的预测精度进行了比较.分析结果表明3种方法的预测准确率PSO-SVM为87.5％、BP-NN为50％、SVM为62.5％.可见,PSO-SVM方法的预测效果要好于BP-NN和SVM,对煤矿煤与瓦斯突出强度预测具有一定的参考价值和指导意义.     

PSO-SVM模型的构建与应用

为了使支持向量机(SVM)获得更好的分类效果,针对人为选择参数的随机性,提出了利用粒子群算法(PSO)进行参数自动选取的优化方法,构建了PSO-SVM模型.在个人信用评估中,通过对粒子适应度函数的设置来控制造成较大损失的第二类误判,应用结果表明:模型在训练和测试样本中的分类精度可以达到95%,第二类误判率分别仅为0.78%和2.02%.利用PSO对SVM中的参数进行优化,可以避免人为选择的随机性,并且在解决分类问题中表现出较好的稳健性.     

基于PSO-SVM算法的长微博贴图识别方法

微博由于字数的限制,当用户需要发较多内容时通常以附图的形式给出,识别包含文本内容贴图的长微博能够为微博研究提供更多有用的数据.在支持向量机（SVM）的基础上结合粒子群算法（PSO）提出了一种识别长微博贴图的PSO-SVM算法.该方法提取长微博贴图的颜色矩和灰度共生矩阵特征,然后利用PSO算法对SVM模型中的误差惩罚参数和核函数进行优化得到最佳分类模型,其最优参数将被用作长微博贴图和非长微博贴图进行分类.实验表明,与传统的基于网格搜索法优化的SVM算法相比,PSO-SVM算法对长微博贴图识别具有更高的准确率和召回率.     

Application of a support vector machine for prediction of slope stability

Slope stability estimation is an engineering problem that involves several parameters. To address these problems, a hybrid model based on the combination of support vector machine(SVM) and particle swarm optimization(PSO) is proposed in this study to improve the forecasting performance. PSO was employed in selecting the appropriate SVM parameters to enhance the forecasting accuracy. Several important parameters, including the magnitude of unit weight, cohesion, angle of internal friction, slope angle, height, pore water pressure coefficient, were used as the input parameters, while the status of slope was the output parameter. The results show that the PSO-SVM is a powerful computational tool that can be used to predict the slope stability.     

基于PSO-SVM模型的城轨列车轮对尺寸预测

为了及时掌握轮对尺寸信息,从而保证列车正常运行,提出了建立粒子群优化支持向量机模型对轮对尺寸进行预测的方法.介绍了粒子群优化算法(PSO)及支持向量机(SVM)的相关概念,并利用粒子群优化算法能够实现快速全局优化的特点对支持向量机进行参数优化,解决了支持向量机参数选择盲目性的问题.以某城轨列车轮径值为研究对象,建立基于PSO-SVM的轮对尺寸预测模型,对轮径值进行预测分析.结果表明,轮径值预测相关度达到0.94,证实了建立的预测模型在轮对尺寸预测方面的可行性及有效性.     

改进PSO-SVM在说话人识别中的应用

为了加快粒子群优化算法的收敛速度,增强全局的搜索能力,通过对粒子群优化算法中惯性权重和全局最优值的分析,提出了一种根据迭代次数而自适应变化的惯性权重的粒子群优化方法。改进后的粒子群算法在防止陷入局部最优的能力方面有了明显的增强,同时,给出了应用粒子群优化算法训练支持向量机的方法,并将其应用于说话人识别。实验结果证实了在说话人识别中改进PSO-SVM方法比其他传统方法能获得更好的识别精度和识别速度。     

应用PSO和SVM的水下航行器黑箱建模

随着新型水下航行器不断涌现,现有水下航行器数学模型已难以与实际模型吻合.为更好了解新型水下航行器实际模型以及预测新型水下航行器运动,提出应用粒子群(particle swarm optimization,PSO)参数寻优和支持向量机(support vector machine,SVM)的水下航行器黑箱建模方法.首先根据水下航行器的运动状态信息和推进器力,应用支持向量机构造出之间的非线性映射关系,然后通过粒子群智能优化算法获得支持向量机的最佳参数组合,进而实现水下航行器的黑箱建模,最后根据推进器力是否时变,分别以新型四旋翼水下航行器的两种空间运动进行实验验证,并以均方根误差作为空间运动预测结果的评价标准.试验结果表明,基于粒子群参数寻优和支持向量机所构建的水下航行器黑箱模型对空间运动预测具有较小的均方根误差,空间运动预测结果与实际运动基本一致,所建黑箱模型与实际模型基本吻合,能有效预测水下航行器运动状态.     

PSO-SVM与BP神经网络组合预测供水系统余氯的方法

针对余氯量在供水系统内非线性变化的特性,建立了PSO-SVM与BP神经网络组合模型对管网末端余氯进行预测分析。该模型通过粒子群优化算法（PSO）,对SVM的特性参数进行优化;采用BP神经网络对模型进行残差修正。通过对单一的BP模型和SVM模型、组合模型的预测精度进行分析。结果表明：组合模型预测比BP和SVM单一预测均方误差分别降低了62.30%、75.29%,平均相对误差降低了55.03%、54.27%。综上所述,该模型具有强大的非线性拟合能力,预测精度高,运行稳定性强,对供水企业控制余氯的投加量和设置二次加氯点有一定的指导作用。     

改进粒子群与支持向量机混合的特征变换

研究了数据挖掘中通过特征变换的数据预处理来提高支持向量机(SVM)分类精度的方法,提出了改进粒子群优化(PSO)和SVM混合的方法. 用推广t统计、Fisher判别式和随机森林的线性加权度量来排序特征,得到预选特征子集,再用启发式信息加速改进PSO搜索特征的线性变换因子,并用二进制PSO对特征变换子集进行特征选择,在后处理中通过格子搜索获取了高精度SVM分类器. 在NIPS 2003的madelon及10个UCI数据集上的实验表明,与有C-SVM分类精度相比,新方法在4个数据集上的精度更高.     

基于PSO-SVM的离心式压缩机转速模型的研究

针对宁夏某石化公司离心式CO2压缩机透平转速预测难以实现问题,引入PSO-SVM回归模型对离心式压缩机透平转速进行预测.分析选取离心式压缩机透平转速作为模型因变量,通过相关分析从采集量中选取高相关度预测因子,运用粒子群算法选择模型最优参数,利用支持向量机的方法建立模型进行预测,与传统的SVM模型进行对比,该模型得到了良好效果,能够有效预测压缩机透平转速.     

Fuzzy neural and chaotic searching hybrid algorithm and its application in electric customers＇s credit risk evaluation

To evaluate the credit risk of customers in power market precisely, the new chaotic searching and fuzzy neural network （FNN） hybrid algorithm were proposed. By combining with the chaotic searching, the learning ability of the FNN was markedly enhanced. Customers＇ actual credit flaw data of power supply enterprises were collected to carry on the real evaluation, which can be treated as example for the model. The result shows that the proposed method surpasses the traditional statistical models in regard to the precision of forecasting and has a practical value. Compared with the results of ordinary FNN and ANN, the precision of the proposed algorithm can be enhanced by 2.2% and 4.5%, respectively.     

Hybrid optimization model of product concepts

Deficiencies of applying the simple genetic algorithm to generate concepts were specified. Based on analyzing conceptual design and the morphological matrix of an excavator, the hybrid optimization model of generating its concepts was proposed, viz. an improved adaptive genetic algorithm was applied to explore the excavator concepts in the searching space of conceptual design, and a neural network was used to evaluate the fitness of the population. The optimization of generating concepts was finished through the ＂evolution - evaluation＂ iteration. The results show that by using the hybrid optimization model, not only the fitness evaluation and constraint conditions are well processed, but also the search precision and convergence speed of the optimization process are greatly improved. An example is presented to demonstrate the advantages of the orooosed method and associated algorithms.     

Identification of large-scale goaf instability in underground mine using particle swarm optimization and support vector machine

An approach which combines particle swarm optimization and support vector machine (PSO–SVM) is proposed to forecast large-scale goaf instability (LSGI). Firstly, influencing factors of goaf safety are analyzed, and following parameters were selected as evaluation indexes in the LSGI: uniaxial compressive strength (UCS) of rock, elastic modulus (E) of rock, rock quality designation (RQD), area ration of pillar (S_p), the ratio of width to height of the pillar (w/h), depth of ore body (H), volume of goaf (V), dip of ore body (α) and area of goaf (S_g). Then LSGI forecasting model by PSO-SVM was established according to the influencing factors. The performance of hybrid model (PSO + SVM = PSO–SVM) has been compared with the grid search method of support vector machine (GSM–SVM) model. The actual data of 40 goafs are applied to research the forecasting ability of the proposed method, and two cases of underground mine are also validated by the proposed model. The results indicated that the heuristic algorithm of PSO can speed up the SVM parameter optimization search, and the predictive ability of the PSO–SVM model with the RBF kernel function is acceptable and robust, which might hold a high potential to become a useful tool in goaf risky prediction research.     

PSO的向量整体修订策略和局部跳出策略

针对传统PSO方法对CEC2005(The 2005 IEEE Congress on evolutionary computation)中的25个benchmark函数搜索效果较差的问题,提出了"向量整体修订"和"局部跳出"两种改进策略。改变PSO方法中粒子在每一维上的修订相互独立的传统机制,按某一概率将粒子作为整体进行修正,当群体最优长时间不变或变化值小于一定阈值时,为跳出局部最优,按某一概率重新定义群体最优或初始化群体。通过实验证明了改进后的PSO方法对CEC2005中的测试问题的有效性。     

Nondestructive testing for crack of tunnel lining using GPR

Lining craze, as a common engineering damage, affects the bearing capacity and the safety of tunnels. To improve the capabilities of the surface crack detection in the tunnel lining, ground penetrating radar (GPR) was employed. The principle and method of GPR for cracks investigation of tunnel lining were expounded. As an application example, some field measurements in order to detect the depth of cracks for the tunnel lining were achieved in a new tunnel. All GPR investigations were made with the antennae of 1 GHz. The cracks of concrete structure were located, and the distributing and depth of cracks are inspected and estimated by GPR. The results of investigation show GPR is accurate and efficient to detect cracks of tunnel lining. Foundation item: Projects(50204012; 50490274) supported by the National Natural Science Foundation of China; project supported by the Postdoctoral Science Foundation of China     

基于改进粒子群算法的组合测试数据生成

针对传统粒子群优化算法生成测试数据容易产生早熟收敛而陷入局部最优的问题,提出一种基于改进粒子群算法的组合测试数据生成算法。该算法在粒子群算法的基础上引入一种惯性权重自适应调整策略,根据粒子的适应度不同采用不同的惯性权重,从而有效的平衡算法的全局和局部搜索能力,增加种群的多样性并提高算法的搜索效率。仿真实验表明该算法与传统粒子群算法相比,所需迭代次数减少,生成组合测试数据速度快。     

冲击地压危险等级预测的PSO-SVM模型

为了对冲击地压进行有效的预测,分析了冲击地压的主要影响因素,建立了基于粒子群优化支持向量机方法(PSO-SVM)的冲击地压危险程度预测模型,并通过实例,对PSO-SVM模型的预测效果进行了检验,同时还分别采用了BP神经网络(BP-NN)和支持向量机方法(SVM)对实例进行了预测,最后对三种方法的预测精度进行了比较分析,结果显示:PSO-SVM方法的预测精度要高于BP-NN和SVM方法的预测精度,可见,PSO-SVM预测方法对煤矿冲击地压危险程度预测具有一定的参考价值和指导意义.     

Dynamic services selection algorithm in Web services composition supporting cross-enterprises collaboration

Based on the deficiency of time convergence and variability of Web services selection for services composition supporting cross-enterprises collaboration, an algorithm QCDSS (QoS constraints of dynamic Web services selection) to resolve dynamic Web services selection with QoS global optimal path, was proposed. The essence of the algorithm was that the problem of dynamic Web services selection with QoS global optimal path was transformed into a multi-objective services composition optimization problem with QoS constraints. The operations of the cross and mutation in genetic algorithm were brought into PSOA (particle swarm optimization algorithm), forming an improved algorithm (IPSOA) to solve the QoS global optimal problem. Theoretical analysis and experimental results indicate that the algorithm can better satisfy the time convergence requirement for Web services composition supporting cross-enterprises collaboration than the traditional algorithms. Foundation item: Project(70631004) supported by the Key Project of the National Natural Science Foundation of China; Project(20080440988) supported by the Postdoctoral Science Foundation of China; Project(09JJ4030) supported by the Natural Science Foundation of Hunan Province, China; Project supported by the Postdoctoral Science Foundation of Central South University, China