基于Lyapunov-Elman的网络控制系统时延预测方法

网络控制系统中的时延是影响系统性能的重要参数,针对基于Internet的网络控制系统中时延预测问题,提出一种最大Lyapunov指数与Elman神经网络结合的预测方法.首先对时延序列进行相空间重构,得到嵌入维数与延迟变量,然后通过最大Lyapunov指数方法与Elman神经网络对时延分别进行一步预测,将两种预测方法的预测结果通过不同的权值系数进行叠加得到最终的时延预测值.最后针对权值系数的寻优问题,提出一种改进的自由搜索算法,其收敛精度与速度都优于标准的自由搜索算法.仿真实验表明,相对于其它预测方法,本文的基于Lyapunov-Elman的时延预测方法具有较高的预测精度与较小的预测误差.     

基于T-S模糊神经网络的淋水井筒温度预测分析

随着矿井开采深度的增加,矿井井底风流温度的预测分析对矿井生产具有重要意义。通过分析影响井底风温的主要因素:地面大气压力、入风温度、入风含湿量以及井筒深度,建立了一种新的T-S模糊神经网络模型,利用MATLAB模拟实现了对井筒的井底风温预测分析。通过实例验证了该方法的可行性,结果表明该方法相比BP神经网络收敛速度快,预测精度高,拟合能力强,符合现场工程应用的需要。     

改进果蝇优化算法优化广义回归神经网络的预测问题研究

为提高传统非线性预测模型的预测精度,提出一种基于改进果蝇优化算法优化广义回归神经网络的预测方法,将果蝇群体分两部分分别进行迭代寻优,从而改进了果蝇优化算法的寻优性能,进而避免了在寻优过程中陷入局部最优。该方法利用改进果蝇优化算法优化广义回归神经网络的径向基函数扩展参数,然后用训练好的广义回归神经网络预测模型进行预测,最后通过订单预测算例进行实证研究。实证研究结果显示,该方法在解决订单预测问题中与未改进的果蝇优化算法优化广义回归神经网络和传统的广义回归神经网络方法对比,具有更高的预测精度和更好的非线性拟合能力。     

基于小波阀值降噪和BP神经网络的超短期风电功率预测

针对超短期风电功率预测问题,考虑了风电场复杂的噪声背景和风电功率的波动性,提出了一种基于小波阀值降噪-BP神经网络的超短期风电功率预测方法。该方法采用近似对称光滑的紧支撑双正交小波db4（Daubechies函数）作为小波基,通过多分辨分析的Mallat算法对历史时序风电功率数据进行3尺度分解。根据Donoho阀值法对各层小波系数进行软阀值降噪处理,再通过小波逆变换重构历史时序风电功率,由BP神经网络对其进行训练,预测目的风电功率序列。仿真算例将该方法与普通BP神经网络方法进行了对比,比较结果证明其预测精度优于后者,具有很好鲁棒性和降噪性能,适用噪声复杂的风电场超短期风电功率在赣预测．     

基于一元线性回归由风速传感器测量值推导巷道平均风速

通过试验数据建立了风速传感器的测量值与平均风速之间的一元线性回归方程,并对拟合效果进行了评价。对参数的判断表明该方程是有效的,可以近似地把风速传感器的测量值转换成平均风速值,并在相同的巷道长度、相同的风流速度、不同断面下用Comsol模拟了风速流场分布情况。     

BP神经网络样本数据预处理应用研究

提出一种新的线性预处理输入数据的方法,即通过线性运算将样本数据的各个字段值统一到同一个数量级,然后结合数值归一化的方法将数据运用到神经网络。在基于信令的漫游用户实时信用度测评及欠费风险超前控制系统中,使用统一字段值的数量级的方法预处理样本数据取得了很好的预测效果。由此,在模式识别和预测领域,统一样本数据的各个字段值的数量级后再进行网络训练可以取得更好的训练效果。     

用于混沌同步的非线性观测器的稳定性分析

首先讨论了时间连续驱动混沌同步非线性观测器的线性化误差动力学方程的稳定性,给出了关于观测器渐近稳定性的判别准则.[KG*2]随后通过理论分析和数值仿真发现,在时间离散驱动下,只要满足一定条件,观测器与原系统仍有可能达到同步,而不管在连续驱动时是否有渐近稳定的同步观测器存在. 最后推导出了时间离散驱动时混沌同步非线性观测器的渐近稳定性的判别准则,仿真实验结果验证了该准则的正确性.     

基于混沌映射的一种交替结构图像加密算法

将分组密码学中的交替结构首先引入到基于混沌映射的图像加密系统中．采用广义猫映射进行像素的置换和扩散，将单向耦合映射格子用于像素替代，两种操作交替执行．在每一轮加密中，通过简单的密钥扩展产生两种子密钥，分别用于不同的混沌映射，密钥长度随加密轮数而变化．解密的算法结构与加密结构相同，仅需按倒序输入解密密钥．安全性分析表明，该加密算法对密钥十分敏感，对多种攻击手段都具有较好的免疫性，执行速率高且代码紧凑，适用于各种软、硬件的图像加密平台．     

基于ConvJANET的航空发动机剩余寿命预测及其不确定性量化

航空发动机技术是衡量一个国家科技水平和工业实力的重要标志,健康状态监测和剩余使用寿命(remaining useful life, RUL)预测技术是航空发动机安全服役、经济运行的重要保障.针对航空发动机RUL预测精度较低、不确定性难以量化的问题,本文提出了一种数据驱动的航空发动机RUL区间预测方法.首先,在ConvJANET框架下构建新的卷积-卷积循环-全连接结构的深度学习模型,逐层提取航空发动机监测数据中的退化特征;其次,利用极大似然思想指导神经网络模型的优化求解,并基于损失函数形式变化的策略训练模型,实现对航空发动机RUL的高精度预测与不确定性量化.将所提出的方法用于分析航空发动机退化数据集,结果表明,对比传统基于蒙特卡洛的方法,本文提出的方法具有更高的RUL预测准确率和更好的置信区间预测性能.     

相空间重构和混沌遗传神经网络融合的矿井涌水量预测研究

为了更准确预测矿井涌水量变化,有效防治矿山水害,本文提出利用相空间重构和混沌遗传神经网络相结合的方法预测矿井涌水量。选用C-C算法确定嵌入维数和延迟时间,通过对时间序列进行相空间重构来判断涌水量时间序列的混沌特性。为避免BP神经网络极易陷入局部解的问题,采用遗传算法对混沌神经网络进行参数优化,构建混沌遗传神经网络预测模型。将构建的模型应用于某矿山-100 m水平巷道涌水量的预测,在理论预测时长内预测最大误差为3.38%,表明该方法能够反映短期内矿井涌水量变化的趋势,相比单纯的混沌BP神经网络预测模型,预测精度有所提高,可为矿山企业的灾害防治提供科学的参考依据。     

Ensemble Forecasting for Complex Time Series Using Sparse Representation and Neural Networks

Based on the concept of ‘decomposition and ensemble’, a novel ensemble forecasting approach is proposed for complex time series by coupling sparse representation (SR) and feedforward neural network (FNN), i.e. the SR‐based FNN approach. Three main steps are involved: data decomposition via SR, individual forecasting via FNN and ensemble forecasting via a simple addition method. In particular, to capture various coexisting hidden factors, the effective decomposition tool of SR with its unique virtues of flexibility and generalization is introduced to formulate an overcomplete dictionary covering diverse bases, e.g. exponential basis for main trend, Fourier basis for cyclical (and seasonal) features and wavelet basis for transient actions, different from other techniques with a single basis. Using crude oil price (a typical complex time series) as sample data, the empirical study statistically confirms the superiority of the SR‐based FNN method over some other popular forecasting models and similar ensemble models (with other decomposition tools). Copyright © 2016 John Wiley & Sons, Ltd.     

Comparison of Near Neighbour and Neural Network in Travel Forecasting

In this paper we confirm the existence of nonlinear dynamics in a time series of airport arrivals. We subsequently propose alternative non‐parametric forecasting techniques to be used in a travel forecasting problem, emphasizing the difference between the reconstruction and learning approach. We compare the results achieved in point prediction versus sign prediction. The reconstruction approach offers better results in sign prediction and the learning approach in point prediction. Copyright © 2015 John Wiley & Sons, Ltd.     

Probabilistic Forecasts of Wind Power Generation by Stochastic Differential Equation Models

The increasing penetration of wind power has resulted in larger shares of volatile sources of supply in power systems worldwide. In order to operate such systems efficiently, methods for reliable probabilistic forecasts of future wind power production are essential. It is well known that the conditional density of wind power production is highly dependent on the level of predicted wind power and prediction horizon. This paper describes a new approach for wind power forecasting based on logistic‐type stochastic differential equations (SDEs). The SDE formulation allows us to calculate both state‐dependent conditional uncertainties as well as correlation structures. Model estimation is performed by maximizing the likelihood of a multidimensional random vector while accounting for the correlation structure defined by the SDE formulation. We use non‐parametric modelling to explore conditional correlation structures, and skewness of the predictive distributions as a function of explanatory variables. Copyright © 2015 John Wiley & Sons, Ltd.     

Forecasting Core Business Transformation Risk Using the Optimal Rough Set and the Neural Network

In the process of enterprise growth, core business transformation is an eternal theme. Enterprise risk forecasting is always an important concern for stakeholders. Considering the completeness and accuracy of the information in the early‐warning index, this paper presents a new risk‐forecasting method for enterprises to use for core business transformation by using rough set theory and an artificial neural network. First, continuous attribute values are discretized using the fuzzy clustering algorithm based on the maximum discernibility value function and information entropy. Afterwards, the major attributes are reduced by the rough sets. The core business transformation risk rank judgement is extracted to define the connection between network nodes and determine the structure of the neural networks. Finally, the improved back‐propagation (BP) neural network learning and training are used to judge the risk level of the test samples. The experiments are based on 265 listed companies in China, and the results show that the proposed risk‐forecasting model based on rough sets and the neural network provides higher prediction accuracy rates than do other widely developed baselines including logistic regression, neural networks and association rules mining. Copyright © 2015 John Wiley & Sons, Ltd.     

Timescale classification in wind forecasting: A review of the state-of-the-art

The intermittency of the wind has been reported to present significant challenges to power and grid systems, which intensifies with increasing penetration levels. Accurate wind forecasting can mitigate these challenges and help in integrating more wind power into the grid. A range of studies have presented algorithms to forecast the wind in terms of wind speeds and wind power generation across different timescales. However, the classification of timescales varies significantly across the different studies (2010–2014). The timescale is important in specifying which methodology to use when, as well in uniting future research, data requirements, etc. This study proposes a generic statement on how to classify the timescales, and further presents different applications of these forecasts across the entire wind power value chain.     

基于ANSYS的石油井架风载的有限元分析

为了研究全自动带压修井机井架承受风载的受力情况,找出井架结构的应力集中点,利用有限元分析软件ANSYS对其进行实体三维建模,施加风载荷并求解,得到结构在各个方向上的应力云图以及变形图;找出结构在承受风载的薄弱处;避免出现较大应力。这对于提高井架的质量非常有益。此方法可以有效地获得和真实工矿相符合的结果;令石油井架稳定性达到了要求;对类似结构的设计分析也具有一定的参考价值。     

Neural network structure identification in inflation forecasting

Neural networks (NNs) are appropriate to use in time series analysis under conditions of unfulfilled assumptions, i.e., non‐normality and nonlinearity. The aim of this paper is to propose means of addressing identified shortcomings with the objective of identifying the NN structure for inflation forecasting. The research is based on a theoretical model that includes the characteristics of demand‐pull and cost‐push inflation; i.e., it uses the labor market, financial and external factors, and lagged inflation variables. It is conducted at the aggregate level of euro area countries from January 1999 to January 2017. Based on the estimated 90 feedforward NNs (FNNs) and 450 Jordan NNs (JNNs), which differ in variable parameters (number of iterations, learning rate, initial weight value intervals, number of hidden neurons, and weight value of the context unit), the mean square error (MSE), and the Akaike Information Criterion (AIC) are calculated for two periods: in‐the‐sample and out‐of‐sample. Ranking NNs simultaneously on both periods according to either MSE or AIC does not lead to the selection of the ‘best’ NN because the optimal NN in‐the‐sample, based on MSE and/or AIC criteria, often has high out‐of‐sample values of both indicators. To achieve the best compromise solution, i.e., to select an optimal NN, the preference ranking organization method for enrichment of evaluations (PROMETHEE) is used. Comparing the optimal FNN and JNN, i.e., FNN(4,5,1) and JNN(4,3,1), it is concluded that under approximately equal conditions, fewer hidden layer neurons are required in JNN than in FNN, confirming that JNN is parsimonious compared to FNN. Moreover, JNN has a better forecasting performance than FNN.