铁水硅含量的混沌粒子群支持向量机预报方法

提出一种基于混沌粒子群优化(CPSO)的支持向量回归机(SVR)参数优化算法, 并使用该算法建立高炉铁水硅含量预测模型(CPSO–SVR), 对某大型钢铁厂高炉铁水硅含量的实际采集数据进行预测, 结果表明基于混沌粒子群优化算法寻优的参数建立的铁水硅含量支持向量回归预测模型具有良好的预测效果. 与最小二乘支持向量回归机(LS–SVR)、使用粒子群优化算法训练的神经网络(PSO–NN)进行比较, CPSO–SVR模型对铁水硅含量进行预测时预测绝对误差小于0.03的样本数占总测试样本数的百分比达到90%以上, 预测效果明显优于PSO–NN, 且比LS–SVR稳定性更强, 可用于高炉铁水硅含量的实际预测, 表明混沌粒子群优化算法是选取SVR参数的有效方法.     

改进粒子群算法优化支持向量机的短期负荷预测

针对支持向量回归机在预测建模中的参数选取问题,提出一种基于混沌自适应策略的粒子群优化支持向量回归机参数的方法.采用混沌映射算法和聚合度自适应判断策略,增强种群的全局寻优性能,提升粒子的多样性,从而避免种群过早收敛.充分考虑天气、节假日、居民消费等因素的影响,提出一种改进的支持向量回归机预测模型并与粒子群算法的支持向量回...     

Multi-fault classification based on support vector machine trained by chaos particle swarm optimization

A novel method of training support vector machine (SVM) by using chaos particle swarm optimization (CPSO) is proposed. A multi-fault classification model based on the SVM trained by CPSO is established and applied to the fault diagnosis of rotating machines. The results show that the method of training SVM using CPSO is feasible, the proposed fault classification model outperforms the neural network trained by chaos particle swarm optimization and least squares support vector machine, the precision and reliability of the fault classification results can meet the requirement of practical application.     

利用PSO-SA混合优化支持向量回归的径流预报模型研究

为了有效提高径流预报的准确度,提出一种有效的融合优化策略,采用基于粒子群和模拟退火算法相结合的混合方法同时优化支持向量回归核函数类型和内核参数,以此建立一种有效的混合优化支持向量回归径流预报模型。提出的方法为核函数选择和参数优化提供了一种有效途径。通过对广西柳州柳江径流实例分析,并与纯粹的支持向量回归模型对比,研究结果表明,该模型预测稳定,具有较高泛化性能和预测准确度,为径流预报提供了一种有效预测方法。     

鲶鱼粒子群算法优化支持向量机的短期负荷预测

为了准确、有效地预测短期负荷,提出了一种鲶鱼粒子群算法优化支持向量机的短期负荷预测模型（BFPSO-SVM）。基于混沌理论对短期负荷时间序列进行相空间重构;将支持向量机参数的组合看作一个粒子位置串,通过粒子间互作找到最优支持向量机参数,并引入“鲶鱼效应”,克服基本粒子群算法的缺点;根据最优参数建立短期负荷预测模型,并对模型性能进行仿真测试。仿真结果表明,相对于其他预测模型,BFPSO-SVM不仅加快了支持向量机参数寻优速度,而且提高了短期负荷预测精度,更适用于短期负荷预测的需要。     

相空间重构和支持向量机相融合的网络流量预测

针对网络流量非线性、突变性和混沌性特点,利用相空间重构和支持向量机参数的天然联系,提出一种相空间重构和支持向量机相融合的网络流量预测方法。将网络流量预测精度作为建模目标,采用粒子群算法对空间重构和支持向量机参数进行组合优化,建立最优网络流量预测模型。仿真实验结果表明,相对于传统网络流量预测方法,该方法更加能够刻画网络流量复杂的变化特点,有效提高了网络流量的预测精度。     

高斯-柯西变异算子优化的LSSVM模型研究

论文针对标准量子粒子群算法易陷入局部极值的问题,提出一种改进的量子粒子优化最小二乘支持向量机的方法。利用高斯变异数的局部开发能力以及柯西变异数的全局搜索能力,在量子粒子群优化算法中,引入高斯-柯西变异算子,帮助算法跳出局部极值。并利用该优化模型进行光伏发电量预测实验,对优化的最小二乘支持向量机模型的预测结果与其他模型预测结果进行比较,结果表明:基于高斯-柯西变异算子的量子粒子群优化的最小二乘支持向量机对光伏发电量的预测具备较好的收敛速度和跳出局部收敛困境的能力。     

Fault diagnosis of sensor by chaos particle swarm optimization algorithm and support vector machine

Fault diagnosis of sensor timely and accurately is very important to improve the reliable operation of systems. In the study, fault diagnosis of sensor by chaos particle swarm optimization algorithm and support vector machine is presented in the paper, where chaos particle swarm optimization is chosen to determine the parameters of SVM. Chaos particle swarm optimization is a kind of improved particle swarm optimization, which can not only avoid the search being trapped in local optimum and but also help to search the optimum quickly by using chaos queues. The wireless sensor is employed as research object, and its four fault types including shock, biasing, short circuit and shifting are applied to test the diagnostic ability of CPSO-SVM compared with other diagnostic methods. The diagnostic results show that CPSO-SVM has higher diagnostic accuracy of wireless sensor than PSO-SVM and BP neural network.     

基于二阶粒子群优化的支持向量机回归在炉龄预测中的应用

针对钢铁企业对炉龄预测精度要求高的特点,提出了基于二阶粒子群优化的支持向量机的炉龄预测方法。利用粒子群智能算法优化支持向量机的回归参数,可以避免按经验选取输入参数的盲目性,能有效地提高预测速度和精度。实例仿真结果表明用该方法对炉龄预测具有很高的精确度,远优于BP神经网络的预测结果。     

多阶灰色支持向量机集成预测模型研究

对灰色预测模型GM(1,1)和支持向量机SVM预测模型进行分析,提出了多阶灰色支持向量机集成预测模型Dm_GM(1,1)-SVM。通过多阶缓冲算子改进灰色预测模型的预测精度,对最终预测值的各个相关指标进行预测;同时,采用粒子群优化算法对支持向量机模型进行径向基核参数和惩罚参数寻优,得到最佳参数对(c,g),从而确定支持向量机的最佳回归模型;最后将各指标预测值作为支持向量机模型的输入,依据预测模型和预测模型的输入值求得预测结果。实验实例表明,多阶灰色支持向量机集成模型和传统的预测模型相比,在本例中预测精度更高,说明多阶灰色预测模型和支持向量机模型相结合在解决实际预测问题中具有实用价值。     

Tax forecasting theory and model based on SVM optimized by PSO

The construction of tax forecasting model is difficult due to its uncertain, non-linear, dynamic and complicated characteristics. It is difficult to describe the non-linear characteristics of tax forecasting by traditional methods. In the study, the novel forecasting method based on the combination of support vector machine (SVM) and particle swarm optimization (PSO) is proposed to the tax forecasting. The non-linear relationship in tax forecasting is efficiently represented by support vector machine, and particle swarm optimization is used to select the training parameters of support vector machine. The tax forecasting model is constructed by support vector machine optimized by particle swarm optimization (PSVM) on the basis of research for the proposed forecasting model. The tax forecasting cases are used to testify the forecasting performance of the proposed model. The experimental results demonstrate that the proposed PSVM model has good forecasting performance.     

PCA和PSO-ELM在高炉铁水硅含量中的预测仿真

炉温的实时预测技术对高炉运转具有重要意义。在高炉炼铁过程中,通常以铁水硅含量来表征高炉热状态。针对硅含量预测效率和精度不足的问题,提出主成分分析和粒子群改进的极限学习机相结合的方法对高炉铁水硅含量进行预测。由于影响铁水硅含量的因素众多,且各因素之间相互影响,通过主成分分析对影响硅含量的输入变量进行降维处理。利用粒子群算法来优化极限学习机的权值和阈值,并以均方根误差作为适应度函数建立预测模型。将提取出的主成分作为模型输入,铁水硅含量作为模型输出。最后比较了极限学习机算法和粒子群改进的极限学习机,实验结果表明改进后的预测模型提高了硅含量预测的准确度,上述方法可为高炉的生产操作提供参考。     

改进PSO-ISVM算法的软件缺陷预测

提出基于改进的粒子群优化支持向量机方法（PSO-ISVM）的测控软件缺陷预测方法。通过引入代价惩罚系数,定义粒子群优化算法中的适应度函数,利用最小化适应度函数值作为优化目标,排除大量的冗余干扰信息,提高对测控软件有缺陷模块的预测准确度,寻找支持向量机的最优参数。通过仿真实例分析测控软件有效性,并与常用缺陷预测方法进行比较,表明该模型能加快软件缺陷预测速度和提高对有缺陷模块的预测准确度。     

改进的PSOGM算法在动态关联规则挖掘中的应用

针对动态关联规则挖掘中支持度向量和置信度向量变化趋势的分析和预测,提出一种改进的粒子群优化的灰色模型应用在动态关联规则挖掘中。由于灰色模型在引入背景值后导致在非平稳序列中的预测精度下降,因此有必要引入参数进行修正,通过在粒子群优化算法中引入二次搜索机制,优化求解灰色模型不同时刻的背景值,从而提高粒子群算法的局部搜索能力,进而提高灰色模型的预测精度。通过在Matlab平台上进行实验仿真,数据集采用超市购物数据,结果表明该方法比原始灰色模型、遗传算法优化的灰色模型和标准的粒子群优化的灰色模型具有更高的预测精度。     

Enhancing the Lithium-ion battery life predictability using a hybrid method

This study contributes to proposing the improved bird swarm algorithm optimization least squares support vector machine (IBSA-LSSVM) model to predict the remaining life of lithium-ion batteries. By improving the prediction accuracy of the model, the safety and reliability of the new energy storage system are improved. In order to avoid the bird swarm algorithm (BSA) getting into the local optimal solution, the levy flight strategy is introduced into the improved bird swarm algorithm (IBSA), which improves the convergence performance of the algorithm. Hence, this study is to verify the effectiveness of the proposed hybrid IBSA-LSSVM model. The following work has been done: (1) test functions are used to test particle swarm optimization (PSO), differential evolution algorithm (DE), BSA and IBSA; (2) the back propagation neural network (BP) model, support vector machine (SVM) model, quantum particle swarm optimization support vector machine (QPSO-SVM) model, BSA-LSSVM model and IBSA-LSSVM model are tested with the B5, B6 and B18 batteries. The following findings are obtained: (1) the five test functions are used to test the PSO, DE, BSA and IBSA algorithms in 20 dimensions, 50 dimensions and 80 dimensions. The results show that the convergence accuracy and convergence stability of IBSA algorithm is higher than those of the other three algorithms; (2) the residual life of B5, B6 and B18 batteries are predicted by the BSA-LSSVM, SVM, QPSO-SVM, BP and IBSA-LSSVM models. The test results show that the root mean square error of the IBSA-LSSVM model for B5 battery is 0.01, the root mean square error for B6 battery is 0.06, and the root mean square error for B18 battery is 0.02. The results show that the prediction accuracy of proposed model is higher than that of the other models.     

Financial time series forecasting using LPP and SVM optimized by PSO

In this paper, a predicting model is constructed to forecast stock market behavior with the aid of locality preserving projection, particle swarm optimization, and a support vector machine. First, four stock market technique variables are selected as the input feature, and a slide window is used to obtain the input raw data of the model. Second, the locality preserving projection method is utilized to reduce the dimension of the raw data and to extract the intrinsic feature to improve the performance of the predicting model. Finally, a support vector machine optimized using particle swarm optimization is applied to forecast the next day’s price movement. The proposed model is used with the Shanghai stock market index and the Dow Jones index, and experimental results show that the proposed model performs better than other models in the areas of prediction accuracy rate and profit.     

多智能体粒子群优化的SVR 模型预测控制

参数的优化选择对支持向量回归机的预测精度和泛化能力影响显著,鉴于此,提出一种多智能体粒子群算法(MAPSO)寻优其参数的方法,并建立MAPSO支持向量回归模型,用于非线性系统的模型预测控制,推导出最优控制率.采用该算法对非线性系统进行仿真,并与基于粒子群算法、基于遗传算法优化支持向量回归机的模型预测控制方法和RBF神经网络的预测控制方法进行比较,结果表明,所提出的算法具有更好的控制性能,可以有效应用于非线性系统控制中.     

A load prediction model for cloud computing using PSO-based weighted wavelet support vector machine

In order to reduce the energy consumption in the cloud data center, it is necessary to make reasonable scheduling of resources in the cloud. The accurate prediction for cloud computing load can be very helpful for resource scheduling to minimize the energy consumption. In this paper, a cloud load prediction model based on weighted wavelet support vector machine(WWSVM) is proposed to predict the host load sequence in the cloud data center. The model combines the wavelet transform and support vector machine to combine the advantages of them, and assigns weight to the sample, which reflects the importance of different sample points and improves the accuracy of load prediction. In order to find the optimal combination of the parameters, we proposed a parameter optimization algorithm based on particle swarm optimization(PSO). Finally, based on the WWSVM model, a load prediction algorithm is proposed for cloud computing using PSO-based weighted support vector machine. The Google cloud computing data set is used to verify the algorithm proposed in this paper by experiments. The experiment results indicate that comparing with the wavelet support vector machine, autoregressive integrated moving average, adaptive network-based fuzzy inference system and tuned support vector regression, the proposed algorithm is superior to the other four prediction algorithms in prediction accuracy and efficiency.     

基于最小二乘支持向量机的耕地面积预测研究*

针对耕地面积数据的小样本、复杂非线性特点,提出一种基于最小二乘支持向量机的耕地面积预测方法。采用相关系数法选择耕地面积的影响因子,通过粒子群优化算法对最小二乘支持向量机参数进行优化,最后建立耕地面积与影响因子之间复杂的非线性关系模型。采用湖南省耕地面积数据对模型性能进行验证,结果表明,相对于参比模型,最小二乘支持向量机提高了耕地面积的预测精度,是一种有效的耕地面积预测方法。     

基于粒子群优化支持向量机的石油需求预测

在能源问题的研究中,石油需求的准确预测对于我国经济管理部门制定石油生产与进口计划、安排相关行业生产计划以及调整产业结构具有非常重要意义。为了实现石油需求准确预测,采用实时准确算法,提出基于粒子群优化支持向量机(PSO-SVM)的石油需求预测方法,PSO-SVM中采用粒子群优化算法优化SVM参数,以获得较优的SVM预测模型。并以我国1990~2007年石油需求数据进行测试与分析,计算实验结果表明,在石油需求预测中,PSO-SVM比BP有着更高的预测精度,为实际需求提供依据。