Lifetime prediction for tantalum capacitors with multiple degradation measures and particle swarm optimization based grey model

A lifetime prediction method for high-reliability tantalum （Ta） capacitors was proposed, based on multiple degradation measures and grey model （GM）. For analyzing performance degradation data, a two-parameter model based on GM was developed. In order to improve the prediction accuracy of the two-parameter model, parameter selection based on particle swarm optimization （PSO） was used. Then, the new PSO-GM（1, 2, co） optimization model was constructed, which was validated experimentally by conducting an accelerated testing on the Ta capacitors. The experiments were conducted at three different stress levels of 85, 120, and 145℃. The results of two experiments were used in estimating the parameters. And the reliability of the Ta capacitors was estimated at the same stress conditions of the third experiment. The results indicate that the proposed method is valid and accurate.

Expressway traffic flow prediction using chaos cloud particle swarm algorithm and PPPR model

Aiming at the real-time fluctuation and nonlinear characteristics of the expressway short-term traffic flow forecasting the parameter projection pursuit regression PPPR model is applied to forecast the expressway traffic flow where the orthogonal Hermite polynomial is used to fit the ridge functions and the least square method is employed to determine the polynomial weight coefficient c.In order to efficiently optimize the projection direction a and the number M of ridge functions of the PPPR model the chaos cloud particle swarm optimization CCPSO algorithm is applied to optimize the parameters. The CCPSO-PPPR hybrid optimization model for expressway short-term traffic flow forecasting is established in which the CCPSO algorithm is used to optimize the optimal projection direction a in the inner layer while the number M of ridge functions is optimized in the outer layer.Traffic volume weather factors and travel date of the previous several time intervals of the road section are taken as the input influencing factors. Example forecasting and model comparison results indicate that the proposed model can obtain a better forecasting effect and its absolute error is controlled within [-6,6] which can meet the application requirements of expressway traffic flow forecasting.

Fractional derivative multivariable grey model for nonstationary sequence and its application

Most of the existing multivariable grey models are based on the 1-order derivative and 1-order accumulation, which makes the parameters unable to be adjusted according to the data characteristics of the actual problems. The results about fractional derivative multivariable grey models are very few at present. In this paper, a multivariable Caputo fractional derivative grey model with convolution integral CFGMC(q, N) is proposed. First, the Caputo fractional difference is used to discretize the model, and the least square method is used to solve the parameters. The orders of accumulations and differential equations are determined by using particle swarm optimization(PSO). Then, the analytical solution of the model is obtained by using the Laplace transform, and the convergence and divergence of series in analytical solutions are also discussed. Finally, the CFGMC(q, N) model is used to predict the municipal solid waste(MSW). Compared with other competition models, the model has the best prediction effect. This study enriches the model form of the multivariable grey model, expands the scope of application, and provides a new idea for the development of fractional derivative grey model.

Comparison of aircraft observations with ensemble forecast model results in terms of the microphysical characteristics of stratiform precipitation

The prediction of the particle number concentration and liquid/ice water content of cloud is significant for many aspects of atmospheric science.However,given the uncertainties in the initial and boundary conditions and imperfections of microphysical schemes,the accurate prediction of these microphysical properties of cloud is still a big challenge.The ensemble approach may be a viable way to reduce forecast uncertainties.In this paper,a large-scale stratiform cloud precipitation process is studied by comparing results of a 10-member ensemble forecast model with aircraft observation data.By means of the ensemble average,the prediction of bulk parameters such as liquid water content and ice water content can be improved in comparison with the control member,but the particle number concentrations are still one to two orders of magnitude less than those from observations.Intercomparison of raindrop size spectra reveals a big distinction between observations and predictions for particles with a diameter less than 1000μm.

A Hybrid Particle Swarm Optimization to Forecast Implied Volatility Risk

The application of optimization methods to prediction issues is a continually exploring field.In line with this,this paper investigates the connectedness between the infected cases of COVID-19 and US fear index from a forecasting perspective.The complex characteristics of implied volatility risk index such as non-linearity structure,time-varying and nonstationarity motivate us to apply a nonlinear polynomial Hammerstein model with known structure and unknown parameters.We use the Hybrid Particle Swarm Optimization(HPSO)tool to identify the model parameters of nonlinear polynomial Hammerstein model.Findings indicate that,following a nonlinear polynomial behaviour cascaded to an autoregressive with exogenous input(ARX)behaviour,the fear index in US financial market is significantly affected by COVID-19-infected cases in the US,COVID-19-infected cases in the world and COVID-19-infected cases in China,respectively.Statistical performance indicators provided by the developed models show that COVID-19-infected cases in the US are particularly powerful in predicting the Cboe volatility index compared to COVID-19-infected cases in the world and China(MAPE(2.1013%);R2(91.78%)and RMSE(0.6363 percentage points)).The proposed approaches have also shown good convergence characteristics and accurate fits of the data.

Research on the mining roadway displacement forecasting based on support vector machine theory

In view of the difficulty in supporting the surrounding rocks of roadway 3-411 ofFucun Coal Mine of Zaozhuang Mining Group, a deformation forecasting model was putforward based on particle swarm optimization.The kernel function and model parameterswere optimized using particle swarm optimization.It is shown that the forecast result isvery close to the real monitoring data.Furthermore, the PSO-SVM (Particle Swarm Optimization-Support Vector Machine) model is compared with the GM(1,1) model and L-M BPnetwork model.The results show that PSO-SVM method is better in the aspect of predictionaccuracy and the PSO-SVM roadway deformation pre-diction model is feasible for thelarge deformation prediction of coal mine roadway.

安徽省生鲜农产品冷链物流需求预测研究

生鲜农产品等冷链产品市场需求快速增长,冷链物流的供给无法满足人们的需求给生鲜农产品带来新的挑战.安徽省作为一个农产品丰富的地区,生鲜农产品的供应对于满足市场需求至关重要.收集了2001~2022年生鲜农产品产量数据,采用反向传播神经网络(Back Propagation Neural Network,BP神经网络)、长短时记忆(long short-term memory,LSTM)、粒子群算法优化的长短期记忆神经网络(Particle Swarm Optimization-Long Short-Term Memory,PSO-LSTM)三种模型进行训练和验证,通过三种模型的对比分析,三种模型相对误差分别为0.13%、0.06%、0.02%.结果表明,PSO-LSTM模型预测精度最高,拟合效果最好,能够有效预测未来四年安徽省生鲜农产品冷链物流需求,以应对不断增长的冷链物流需求压力.

基于VMD的长江航运干散货运价指数预测

我国长江航运干散货运价指数(YBFI)呈现非线性、非平稳性等波动特征,传统的单一预测模型和组合预测法难以获得较好的预测效果。为此,基于“分解-重构-预测-集成”的思想,提出一种基于变分模态分解(VMD)的YBFI组合预测模型构建方法。选用变分模态分解(VMD)将原始运价指数序列分解为多个模态分量,并通过聚类分析将分量重构为高频、中频、低频和趋势项,对重构后的序列波动特点进行解释。选用BPNN对高频项和低频项进行预测,采用PSO-SVM方法对中频项和趋势项进行预测,最后将重构项预测结果相加集成得到最终预测值。实证结果表明,构建的基于VMD的组合预测模型比SVM、BPNN、ARIMA、PLS等单一预测模型,以及未优化的VMD组合模型、VMD-BP等组合模型具有更好的预测效果。

基于机器学习的粗轧入口板坯温度预测

为提高热连轧产品质量和性能,对粗轧入口温度建模预测研究。建立一种基于机器学习的粗轧入口板坯温度预测模型,首先对传感器采集的粗轧入口板坯温度值进行预处理;其次采用智能粒子群优化算法(particle swarm optimization,PSO),对最小二乘支持向量机(least squares support vector machine,LSSVM)预测模型进行寻优;最后建立一种PSO-LSSVM粗轧入口板坯温度预测模型。通过大量数据训练优化仿真,结果表明,此模型预测精度高,拟合效果好。

A novel fractional grey forecasting model with variable weighted buffer operator and its application in forecasting China's crude oil consumption

Oil is an important strategic material and civil energy.Accurate prediction of oil consumption can provide basis for relevant departments to reasonably arrange crude oil production,oil import and export,and optimize the allocation of social resources.Therefore,a new grey model FENBGM(1,1)is proposed to predict oil consumption in China.Firstly,the grey effect of the traditional GM(1,1)model was transformed into a quadratic equation.Four different parameters were introduced to improve the accuracy of the model,and the new initial conditions were designed by optimizing the initial values by weighted buffer operator.Combined with the reprocessing of the original data,the scheme eliminates the random disturbance effect,improves the stability of the system sequence,and can effectively extract the potential pattern of future development.Secondly,the cumulative order of the new model was optimized by fractional cumulative generation operation.At the same time,the smoothness rate quasi-smoothness condition was introduced to verify the stability of the model,and the particle swarm optimization algorithm(PSO)was used to search the optimal parameters of the model to enhance the adaptability of the model.Based on the above improvements,the new combination prediction model overcomes the limitation of the traditional grey model and obtains more accurate and robust prediction results.Then,taking the petroleum consumption of China's manufacturing industry and transportation,storage and postal industry as an example,this paper verifies the validity of FENBGM(1,1)model,analyzes and forecasts China's crude oil consumption with several commonly used forecasting models,and uses FENBGM(1,1)model to forecast China's oil consumption in the next four years.The results show that FENBGM(1,1)model performs best in all cases.Finally,based on the prediction results of FENBGM(1,1)model,some reasonable suggestions are put forward for China's oil consumption planning.

基于流溪河模型的水库洪水预报方案

中国是世界上洪水灾害发生最频繁的国家之一。建立准确可靠的洪水预报程序是应对洪涝灾害的关键。中国南方的某水库流域属于亚热带季风气候,春季雨水较多,夏季暴雨集中,秋季有台风。坝址洪水多为中小型洪水,具有典型的山洪上升陡、下降缓的特点。为了探索流溪河模型在某水库洪水预测中的适用性,基于DEM、土地利用和土壤类型数据,建立了水库流溪河预测模型,并选取1个典型的洪水事件,利用粒子群优化算法对参数进行优化,并通过模拟其他洪水验证了模型的准确性。

基于PSO算法的新安江模型在永宁河洪水预报中的应用研究

根据嘉陵江一级支流永宁河流域的气候特征,利用永宁镇水文站2002-2021年连续20年的逐日水文观测资料,基于粒子群算法(PSO)对新安江模型进行参数率定,基于此对永宁镇水文站日流量进行模拟,探讨该模型在永宁镇水文站洪水预报中的应用。结果表明:新安江模型在永宁镇水文站径流模拟中的成果较好,率定期确定性系数平均值为0.53,合格率为71%,验证期验证期确定性系数平均值为0.59,合格率为100%,模拟成果合格率达到乙级,确定性系数达到丙级预报精度,符合规范要求,可用于该站的洪水预报,为该站洪水预报提出了新的方向,并为新安江模型在嘉陵江上游应用积累了经验。

基于集成算法的工业增加值预测模型研究

我国工业经济的快速发展使得对工业增加值的准确预测成为至关重要的任务,工业增加值在经济中起着举重若轻的作用,其有效的现时预测有助于及时分析宏观经济走向。研究通过应用粒子群优化算法(PSO)对梯度提升决策树(GBDT)、随机森林回归(RFR)、LightGBM、Adaboost、XGBoost和CatBoost等六种集成算法的关键参数进行调整,以提高这些算法在工业增加值预测中的性能,并选取MSE、MAE、精度作为模型评价指标。实验结果显示:对比粒子群优化后的模型指标,依据模型预测性能的优劣情况将其排序:XGBoost>AadBoost>CatBoost>RFR>LightGBM>GBDT。基于粒子群优化算法的XGBoost模型在工业增加值预测中表现出更好的预测效果,为提高工业经济预测的准确性提供了有力支持。

风电场风速的神经网络组合预测模型

针对BP神经网络、RBF神经网络和粒子群BP神经网络在风电场风速预测中存在的问题,提出一种基于遗传算法优化神经网络的风速组合预测模型。该模型为单输出的3层前馈网络,将3种神经网络的预测结果与预测结果平均值作为神经网络的输入,将实际风速值作为神经网络输出,使学习后的网络具有预测能力。该模型能降低单一模型的预测风险,提高预测精度。仿真结果表明,所提出的组合预测模型的精度高于其中任一单一模型,也高于传统的线性组合预测模型。

基于粒子群优化的RBF神经网络交通流预测

交通流量预测一直是实时自适应交通控制的关键问题。以城市道路网络中典型的两相邻交叉口为研究对象,提出了基于粒子群优化的RBF神经网络的信号交叉口交通流量预测模型。该模型以RB F神经网络为基础,采用分组优化策略,用粒子群优化算法对基函数的中心、方差和RBF网络权值进行优化,从而提高了网络的预测精度。通过仿真,并与其他算法对比,表明了本文方法的有效性。

基于改进粒子群-径向基神经网络模型的短期电力负荷预测

为了准确、快速、高效地预测电网短期负荷,提出了改进的粒子群–径向基神经网络算法。用改进的粒子群算法训练径向基神经网络,实现了径向基函数神经网络的参数优化。建立了短期电力负荷预测模型,综合考虑气象、天气、日期类型等影响负荷的因素进行短期负荷预测。算例结果表明,该算法优于径向基神经网络法和粒子群–径向基网络算法,克服了径向基网络和粒子群优化方法的缺点,改善了径向基神经网络的泛化能力,输出稳定,预测精度高,收敛速度快,平均百分比误差可控制在1.2%以内。

基于聚类经验模态分解和最小二乘支持向量机的短期风速组合预测

从分析风速序列的非线性和非平稳性特征出发,将一种基于聚类经验模态分解(EEMD)和最小二乘支持向量机(LSSVM)的组合预测模型引入到风速预测中。首先使用聚类经验模态分解将风速序列分解为一组相对平稳的子序列,以减轻不同趋势信息间的相互影响;然后运用最小二乘支持向量机对各子序列分别建模预测,为降低预测风险,使用自适应扰动粒子群算法(ADPSO)和模型学习效果反馈机制对LSSVM预测模型的输入维数和超参数进行联合优化;最后将各个子序列的预测结果叠加得到预测风速。实例研究表明,本文所提的组合预测模型可以有效挖掘风速序列特性,具有较高的预测精度。

粒子群优化灰色模型在负荷预测中的应用

针对电力系统负荷特性,分析灰色模型GM(1,1)的应用局限性,引入向量α改进灰色模型背景值序列的计算公式,从而构建了适应性更强的GM(1,1,α)模型。应用粒子群优化算法非线性全局寻优能力来求解最优α值,提出了基于粒子群优化算法的灰色模型PSOGM,并给出了电力负荷预测的应用实例。实例证明PSOGM模型具有较高的预测精度和较广的应用范围。

自适应粒子群优化灰色模型的负荷预测

针对传统灰色预测模型GM(1,1)在预测增长较快的电力负荷时预测效果变差这一局限性,引入了比标准粒子群优化算法效率更高的自适应粒子群优化算法,并与GM(1,1)模型相结合,利用自适应粒子群算法求解GM(1,1)模型中的参数a和u,提出一种自适应粒子群优化灰色模型。通过对四个地区的用电量进行实例仿真,证明该模型具有较广的适用范围和较高的预测精度。

基于粒子群改进BP神经网络的组合预测模型及其应用

针对应用广泛的传统人工智能预测BP(Back propagation)神经网络自身局限以及其在处理中长期复杂预测问题中需要样本数量大、泛化能力弱等不足,提出利用粒子群算法优化BP神经网络的学习算法,在此基础上,利用灰色预测方法和自回归移动平均模型(ARIMA)时序预测对历史数据进行初步预测,对中长期预测中数据趋势项和随机项进行模拟;将初步预测的结果作为改进BP神经网络的输入,在此基础上进行训练和预测,构建基于改进BP网络的组合预测模型。以我国1978-2007年能源需求数据为样本,进行实例分析。结果表明:组合预测模型预测精度较BP神经网络、灰色预测方法和ARIMA预测方法分别提高4.8%,6.1%和5.3%,验证了组合预测方法在中长期预测问题处理中的有效性。