基于APSO-WLSSVR的水质预测模型

为解决传统预测方法和标准最小二乘支持向量回归机(least squares support vector regression, LSSVR)在水质预测中存在预测精度低、鲁棒性差等问题,提出了自适应粒子群优化加权最小二乘支持向量回归机(adaptive particle swarm optimization weighted least squares support vector regression, APSO-WLSSVR)的水质预测模型。根据样本对模型重要性不同为各样本赋予不同权重,建立了加权最小二乘支持向量回归机(weighted least squares support vector regression, WLSSVR),实现对样本数据“重近轻远” 的优化选择,避免标准LSSVR算法因没有考虑样本重要性差异致使预测精度低的问题;采用自适应粒子群优化算法对模型参数组合进行优化选择,克服了标准LSSVR算法因试凑法获取参数的盲目性和人为因素的影响。为验证该模型的性能,对江苏省宜兴市集约化河蟹养殖水质进行预测,并与其他预测方法对比分析,结果表明该模型预测精度明显提高,还具有较好的鲁棒性和泛化能力,能够满足集约化水产养殖水质管理的实际需要。     

基于多种神经网络的水沙预测模型研究

为解决水沙变化机理中水库泥沙难以预测的问题,采用支持向量回归（support vector regression, SVR)、遗传算法-支持向量回归（genetic algorithm-support vector regression, GA-SVR）、粒子群优化-支持向量回归（particle swarmoptimization-supportvectorregression,PSO-SVR）、最小二乘支持向量回归（leastsquaressupportvector regression, LSSVR)等模型进行研究,并将模型应用于黑孜水库水文站的年径流量及年输沙量序列的预测分析。数据资料序列划分为85%的训练数据和15%的测试数据,使用均方根误差、相关系数、平均绝对百分比误差、纳什系数这4项指标来评价模型的精度,得到的结果可以为水库的短期调度和长期管理提供帮助。结果表明：LSSVR算法是4种模型中最优的,其误差最小,精度最高;构建的模型可为其他地区的水沙预测提供参考。     

基于PSO-WSVR的短期水质预测模型研究

针对传统方法很难建立精确的非线性水质预测模型的情况,提出了基于粒子群优化加权支持向量回归机(PSO-WSVR)的水质短期预测模型.在建模过程中,根据各样本重要性的差异,给各个样本的惩罚系数赋予不同权重,改进了标准支持向量回归机算法,克服了标准支持向量回归算法因不同样本均采用相同权重造成预测精度低的问题,并采用粒子群优化算法对加权支持向量回归机参数组合进行自适应优化,模型收敛速度明显加快.运用PSO-WSVR模型对江苏宜兴市集约化河蟹养殖池塘水质进行预测,与标准支持向量回归机和BP神经网络对比分析.结果表明,该模型性能可靠、泛化能力强,预测精度高,为集约化水产养殖水质短期预测提供了一种新思路.     

基于小波变换和AWLS-SVM的短期负荷预测

提出了一种基于小波变换和自适应加权最小二乘支持向量机(AWLS-SVM)的电力系统短期负荷预测方法。针对负荷变化具有拟周期性和随机性的特点,本方法先将负荷值利用小波变换分解为几个低频段的拟周期量和一个高频段随机量,然后根据各分量特点应用AWLS-SVM模型进行预测,最后小波重构各分量获得预测结果。实例预测结果表明该方法具有较高的预测精度。     

基于新息的多参量混沌时间序列LS-SVR加权预测

复杂系统常常依赖于通过观测所获得的多参量混沌时间序列进行预测分析.论文借鉴单参量混沌时间序列预测的思路,考虑全部相关参量混沌时间序列中的信息,以实现多参量混沌时间序列的相空间重构.同时,基于新息优先原理和支持向量机理论,结合混沌时间序列发展变化的规律,提出分别利用相空间重构后长期多样本和近期少样本构建2个自适应最小二乘支持向量回归预测模型进行加权预测的观点,并给出了以预测均方根误差最小为目标函数的模型参数混沌优化方法.论文以某飞机转子部件磨损故障的3个相关参量的仿真混沌时间序列为例进行了预测实验,结果表明文中方法有较好的预测精度,是一种有效的预测方法.     

基于免疫算法优化LSSVM的短时交通流预测

为了智能化解决城市道路交通系统存在的问题,提高短时交通流预测的准确性,采用免疫算法优化的最小二乘支持向量机(LSSVM)建立短时交通流量预测模型。利用免疫算法对LSSVM中的惩罚因子和核函数参数进行优化,得到最优预测模型。以车辆行驶平均速度和占有率作为模型输入,交通流量作为输出进行预测仿真试验。试验结果表明:本文采用的优化LSSVM模型进行仿真试验的预测误差有所减小,输出结果更接近真实值。     

基于DEG-WO-LSSVM模型的用电负荷预测

为了提高负荷预测的拟合精度,提出一种基于优化灰狼算法的最小二乘支持向量机负荷预测模型,针对标准灰狼算法精度低、收敛速度慢、易陷入局部最优的缺点,采用差分算法优化标准灰狼算法。利用改进的灰狼算法优化最小二乘支持向量机的两个主要参数,建立功率负荷预测研究模型。通过实例分析获得负荷预测结果,利用三种评价指标对比了四种算法模型。实验表明,改进灰狼算法优化最小二乘支持向量机的改进评价指标数值较低,拟合曲线精度更高。     

电力系统中长期负荷预测改进算法分析

讨论了基于支持向量机的电力系统负荷预测模型建模方法.通过对模型结构的分析,提出了最小二乘支持向量机算法学习参数的选取方法.结合粒子群优化算法,给出了粒子群优化对最小二乘支持向量机系数优化选择的方法.采用某省的经济、人口、天气和电价等实证数据对几种预测方法进行比较分析,算例结果表明,所提出的方法可以加快计算速度,并有效提高预测精度.     

基于鲸鱼优化参数的最小二乘支持向量机短期负荷预测方法

大量分布式能源站的出现以及电动汽车的普及,给电力系统的安全、经济运行带来影响的同时,传统的负荷预测方法也面临挑战。针对这个问题,提出了利用鲸鱼算法优化最小二乘支持向量机（Whale Optimization Algorithm-Least Squares Support Vector Machine,WOA-LSSVM）进行短期电力系统负荷预测。利用鲸鱼算法全局寻优能力强、收敛速度快的优点,弥补最小二乘支持向量机（Least Squares Support Vector Machine,LSSVM）选参的盲目性,提高LSSVM的负荷预测精度。采用WOA-LSSVM对2013年浙江某地区历史负荷数据预测未来1 d的负荷,并与粒子群优化最小二乘支持向量机模型和标准LSSVM模型预测结果对比。结果表明,基于鲸鱼优化LSSVM的短期负荷预测具有较高的预测精度,相对误差较小。     

Thrust estimator design based on least squares support vector regression machine

In order to realize direct thrust control instead of traditional sensor-based control for aero-engines,it is indispensable to design a thrust estimator with high accuracy,so a scheme for thrust estimator design based on the least square support vector regression machine is proposed to solve this problem. Furthermore,numerical simulations confirm the effectiveness of our presented scheme. During the process of estimator design,a wrapper criterion that can not only reduce the computational complexity but also enhance the generalization performance is proposed to select variables as input variables for estimator.     

和声搜索最小二乘支持向量机预测模型及其应用

为了改进目前最小二乘支持向量机(LSSVM)参数选择的盲目性,将和声搜索(Harmony Search)算法引入到最小二乘支持向量机中来.利用具有全局优化功能的和声搜索算法对LSSVM中正则化参数γ和核函数参数σ的进行自动优选,提出了和声搜索最小二乘支持向量机(Harmony Search Least Squares Support Vector Machine,HS-LSSVM)算法.通过对丰满大坝位移的建模预测并和BP神经网络模型及传统统计回归模型的分析比较,表明HS-LSSVM模型具有更小的预测误差和更高的预测精度.     

Time series online prediction algorithm based on least squares support vector machine

Deficiencies of applying the traditional least squares support vector machine （LS-SVM） to time series online prediction were specified. According to the kernel function matrix＇s property and using the recursive calculation of block matrix, a new time series online prediction algorithm based on improved LS-SVM was proposed. The historical training results were fully utilized and the computing speed of LS-SVM was enhanced. Then, the improved algorithm was applied to timc series online prediction. Based on the operational data provided by the Northwest Power Grid of China, the method was used in the transient stability prediction of electric power system. The results show that, compared with the calculation time of the traditional LS-SVM（75 1 600 ms）, that of the proposed method in different time windows is 40-60 ms, proposed method is above 0.8. So the improved method is online prediction. and the prediction accuracy（normalized root mean squared error） of the better than the traditional LS-SVM and more suitable for time series online prediction.     

基于多目标驱动人工蜂群算法的疏散仿真模型

提出一种基于多目标驱动蜂群算法的疏散仿真模型.在人工蜂群算法的基础上,对跟随蜂设置视野搜索半径,将视野范围内适应值最优个体作为其视觉引领蜂,从而减少跟随蜂选择引领蜂的盲目性.提出多目标驱动人工蜂群算法,即由惯性引领蜂、全局最优蜂、个体历史最优蜂、视觉引领蜂共同对跟随蜂的移动方向进行引导,从而使跟随蜂的移动路径得到进一步...     

Forecasting model of residential load based on general regression neural network and PSO-Bayes least squares support vector machine

Firstly,general regression neural network(GRNN) was used for variable selection of key influencing factors of residential load(RL) forecasting.Secondly,the key influencing factors chosen by GRNN were used as the input and output terminals of urban and rural RL for simulating and learning.In addition,the suitable parameters of final model were obtained through applying the evidence theory to combine the optimization results which were calculated with the PSO method and the Bayes theory.Then,the model of PSO-...     

基于最小二乘支持向量机的时用水量预测模型

针对神经网络存在结构较难确定,训练易陷入局部最优以及容易过学习等问题,提出将最小二乘支持向量机用于预测时用水量.最小二乘支持向量机(LSSVM)基于结构风险最小化,能够较好地协调经验风险最小化和学习机器VC维之间的关系,并且LSSVM在支持向量机(SVM)的基础上,通过将价值函数改为最小二乘价值函数以及用等式约束代替不等式约束,将求解的二次规划问题转变为一组等式方程,采用径向基核函数,得到LSSVM模型的待定参数比标准支持向量机少,仅为2个.根据时用水序列具有周期性和趋势性的特点,建立了基于最小二乘支持向量机的时用水量模型.实例分析表明,与基于BP网络的时用水量模型相比,基于最小二乘支持向量机的时用水量模型具有更强的预测能力.     

基于最小二乘支持向量机的模糊时序分析方法

利用最小二乘支持向量机良好的分类和函数估计能力，提出了一种新的模糊时序分析方法。该方法包括两部分：在模糊时序处理部分通过建立启发式规则、模糊变量、论域、模糊集合和隶属度函数，完成历史数据的模糊化；最小二乘支持向量机处理部分替代传统的模糊关系计算，对模糊化的历史数据进行分析，然后去模糊化得到最后的预测结果。与多种传统模糊时序分析方法的对比试验表明，该方法充分利用了支持向量机较好的推广性能等优点，具有更高的精度和较好的泛化效果。     

Fuzzy least squares support vector machine soft measurement model based on adaptive mutative scale chaosimmune algorithm简

In order to enhance measuring precision of the real complex electromechanical system,complex industrial system and complex ecological management system with characteristics of multi-variable,non-liner,strong coupling and large time-delay,in terms of the fuzzy character of this real complex system,a fuzzy least squares support vector machine（FLS-SVM） soft measurement model was established and its parameters were optimized by using adaptive mutative scale chaos immune algorithm.The simulation results reveal that fuzzy least squares support vector machines soft measurement model is of better approximation accuracy and robustness.And application results show that the relative errors of the soft measurement model are less than 3.34%.     

Fault diagnosis of power-shift steering transmission based on multiple outputs least squares support vector regression

A method of multiple outputs least squares support vector regression (LS-SVR) was developed and described in detail, with the radial basis function (RBF) as the kernel function. The method was applied to predict the future state of the power-shift steering transmission (PSST). A prediction model of PSST was gotten with multiple outputs LS-SVR. The model performance was greatly influenced by the penalty parameter γ and kernel parameterσ2 which were optimized using cross validation method. The training and prediction of the model were done with spectrometric oil analysis data. The predictive and actual values were compared and a fault in the second PSST was found. The research proved that this method had good accuracy in PSST fault prediction, and any possible problem in PSST could be found through a comparative analysis.     

基于人工蜂群算法的边坡地震可靠度分析

通过输入埃而森特罗波(EL Centro)地震波对边坡进行有限元瞬态分析,得到边坡在各个时刻的应力值.然后利用Mohr-Coulomb破坏准则,得到对应某一起始点的滑动面.并用瑞典条分法计算边坡安全系数的公式,使其作为目标函数,经过人工蜂群算法进行优化得到起始点,从而确定了滑动面并计算出边坡安全系数.最后,对各个时刻的边坡安全系数进行统计分析,得到边坡失效概率.