基于小波非线性自回归网络的水文预测模型

不同时间尺度上的水文序列预测在水资源调配和防洪减灾决策中起着重要的作用。提出了一种基于小波分解和非线性自回归神经网络相结合的水文时间序列预测模型(WNARN)。运用Daubechies 5(db5)离散小波将水文序列数据分解为低频和高频子序列,作为非线性自回归神经网络模型(NARN)的输入变量,贝叶斯正则化优化算法用来泛化网络,训练模型对各子序列进行模拟预测,预测值经db5小波重构后得到原序列预测值。利用渭河流域三个水文站40多年的月径流量序列对所提出的WNARN模型进行验证和向前48步的预测能力测试,并与单一NARN模型的验证和预测结果进行对比。结果显示在相同的网络结构下所提出的方法能够显著提高水文序列的预测精度、预测周期及对重大水文事件的预测性,具有较高的泛化能力。     

基于EMD和RBFNN的冷负荷组合预测模型

针对冷负荷预测问题,提出了一种基于相空间重构（PSR）、经验模态分解(EMD) 和径向基神经网络(RBFNN) 的 冷负荷组合预测模型。该模型首先利用经验模态分解方法,把冷负荷序列分解为少数模态分量,然后利用分组分量法将其分 为多个高频子分量、总低频分量和残余量,最后以PSR为基础对各分量利用RBFNN方法建模并将预测结果重构。该方法应 用于实际冷站负荷预测后,与单一RBFNN、SVM、LSSVM及基于EMD的SVM、基于EMD的RBFNN5类方法进行比较,结果 表明该方法对冷负荷预测精度有明显提高。     

Time series forecasting based on wavelet decomposition and feature extraction

Time series forecasting is one of the most important issues in numerous applications in real life. The objective of this study was to propose a hybrid neural network model based on wavelet transform (WT) and feature extraction for time series forecasting. The motivation of the proposed model, which is called PCA-WCCNN, is to establish a single simplified model with shorter training time and satisfactory forecasting performance. This model combines the principal component analysis (PCA) and WT with artificial neural networks (ANNs). Given a forecasting sequence, order of the original forecasting model is determined firstly. Secondly, the original time series is decomposed into approximation and detail components by employing WT technique. Then, instead of using all the components as inputs, feature inputs are extracted from all the sub-series obtained from the above step. Finally, based on the extracted features and all the sub-series, a famous neural network construction method called cascade-correlation algorithm is applied to train neural network model to learn the dynamics. As an illustration, the proposed model is compared with two classical models and two hybrid models, respectively. They are the traditional cascade-correlation neural network, back-propagation neural network, wavelet-based cascade-correlation network using all the wavelet components as inputs to establish one model (WCCNN) and wavelet-based cascade-correlation network with combination of each sub-series model (WCCNN multi-models). Results obtained from this study indicate that the proposed method improves the accuracy of ANN and can yield better efficiency than other four neural network models.

     

Chaotic time series method combined with particle swarm optimization and trend adjustment for electricity demand forecasting

Electricity demand forecasting plays an important role in electric power systems planning. In this paper, nonlinear time series modeling technique is applied to analyze electricity demand. Firstly, the phase space, which describes the evolution of the behavior of a nonlinear system, is reconstructed using the delay embedding theorem. Secondly, the largest Lyapunov exponent forecasting method (LLEF) is employed to make a prediction of the chaotic time series. In order to overcome the limitation of LLEF, a weighted largest Lyapunov exponent forecasting method (WLLEF) is proposed to improve the prediction accuracy. The particle swarm optimization algorithm (PSO) is used to determine the optimal weight parameters of WLLEF. The trend adjustment technique is used to take into account the seasonal effects in the data set for improving the forecasting precision of WLLEF. A simulation is performed using a data set that was collected from the grid of New South Wales, Australia during May 14–18, 2007. The results show that chaotic characteristics obviously exist in electricity demand series and the proposed prediction model can effectively predict the electricity demand. The mean absolute relative error of the new prediction model is 2.48%, which is lower than the forecasting errors of existing methods.     

Gaussian小波SVM及其混沌时间序列预测

为了提高混沌时间序列的预测精度,针对小波有利于信号细微特征提取的优点,结合小波技术和SVM的核函数方法,提出基于Gaussian小波SVM的混沌时间序列预测模型.证明了偶数阶Ganssian小波函数满足SVM平移不变核条件,并构建相应的Gaussian小波SVM.时混沌时间序列进行相空间重构,将重构相空间中的向量作为SVM的输入参量.用Ganssian小波SVM与常用的径向基SVM及Morlet小渡SVM进行对比实验,通过对Chen's混沌时间序列和负荷混沌时间序列的预测,结果表明,Ganssian小波SVM的效果比其他两种SVM更好.     

基于小波变换和混沌理论的复杂系统状态预测方法研究

应用小波变换和混沌理论对复杂系统状态预测方法进行了研究．首先，应用小波变换对系统的特征参数序列进行分解，得到低频部分和高频部分．然后，对低频部分和高频部分做进一步分析，以确认低频部分和高频部分都存在混沌特性．再应用混沌理论分别建立低频部分和高频部分的预测模型，对低频部分和高频部分进行预测．最后，应用小波理论对混沌模型预测的结果予以重构，实现对系统特征参数序列的预测．实例研究表明，此方法具有较高的预测精度，可有效地应用于复杂系统的状态预测和故障趋势预测分析．     

微振动语音信号检测的干扰背景分析研究

将小波变换和非线性动力学研究方法相结合,分析研究了在微振动语音信号检测中两类干扰背景的特性及其对检测结果的影响,讨论了两类情况下的解决方法。先通过小波分解得到原始时间序列的低频和高频部分,再分别对其进行处理。结果表明,对随机噪音干扰信号而言,可以通过放大低频、抑制高频来增强微弱信号;而对于强混沌干扰背景信号,混杂信号低频部分和干扰背景的奇异吸引子最相似,因此需要通过抑制低频、放大高频来增强微弱信号。最后通过Matlab音频仿真证明了该结论。     

基于局部熵的小波变换图像融合算法

研究基于小波变换的图像融合算法。首先对待融合图像进行小波多尺度分解,得到小波的分解系数,对高频分量和低频分量系数采取不同的融合规则。低频部分系数融合采用简单加权平均法,高频部分系数融合采用基于邻域熵值的方法。对融合后系数进行逆小波变换得到重构图像,融合后图像清晰、对比度好。与最大绝对值法、领域方差法作比较。最后通过熵、标准差、平均梯度值等参数对融合后的图像进行定量分析。实验数据表明提出的方法是切实可行的。     

基于小波变换阈值决策的混沌信号去噪研究

基于多分辨率分析的思想，提出了一种改进的小波去噪方法．通过细化小波分解信号，能够更有效地提取出非线性系统信号．同时，根据不同尺度下小波信号的信噪比灵活地选取阈值，更加符合实际情况，有利于改善去噪效果．对Lorenz混沌时间序列和黄河年径流时间序列分别进行了仿真，结果表明了所提方法的有效性．     

TAGUCHI'S DESIGN OF EXPERIMENT IN COMBINATION SELECTION FOR A CHAOTIC TIME SERIES FORECASTING METHOD USING ENSEMBLE ARTIFICIAL NEURAL NETWORKS

Taguchi's design of experiment, an effective approach to identify factor-level combinations, was utilized to improve the result of a proposed chaotic time series forecasting method. In the proposed method, a residual analysis using a combination of embedding theorem and ensemble neural networks was employed to forecast chaotic time series. The time series was reconstructed into proper phase space points and fed into the first neural network. The network was trained to predict the future value of phase space points and chaotic time series. The analysis of residuals of the predicted time series showed that in many events they demonstrate chaotic behaviour. The residuals were treated as a new chaotic time series and reconstructed. A new network was trained to predict the future of the residual time series. The residual analysis was repeated several times. Finally, the last network was trained using a forecast value of the original time series and residuals as input and the original time series as output. The final network was used to capture the relationship between the forecast values of the original time series and residuals and the original time series. A systematic approach is introduced using Taguchi's method to improve the combination selection of networks and their parameters. The method was applied to some real-life chaotic time series. The experimental results confirmed that the proposed method performed more effectively and accurately compared to the same method using randomized factor-level combinations and other existing forecasting methods in the literature.     

基于局部投影和提升小波的混沌信号降噪

根据信号和噪声的不同特性,提出了一种基于局部投影和提升小波的混沌信号降噪方法。该方法利用提升小波变换得到实际混沌信号的近似系数,对其进行相空间重构,与实际混沌信号的重构相空间进行比较,来选取局部投影的邻域,在局部邻域内进行奇异谱分析,利用代表吸引子的主分量来重构混沌信号。通过对Lorenz模型和月太阳黑子进行仿真分析,证实了提出方法对混沌信号降噪的有效性。     

基于EMD-TCN-ELM的短期电力负荷预测

为降低负荷序列的复杂性,利用EMD分解方法得到不同的分量.为降低训练时间和减小分量逐个预测所带来的累计误差,利用分量过零率大小将分量重构为高频分量和低频分量,利用TCN模型预测负荷的高频分量,利用极限学习机ELM预测负荷低频分量.通过实验将所提模型EMD-TCN-ELM分别与3个单模型TCN、ELM、LSTM和3个混合模型EMD-TCN、EMD-ELM、EMD-LSTM比较,其MAPE分别降低0.538%, 1.866%, 1.191%,0.026%, 1.559%, 0.323%,所提模型的预测精度最高.且所提模型在预测精度前3的模型中训练时间最短,验证了所提模型在负荷预测精度和训练时间方面的优越性.     

Rainfall data analyzing using moving average (MA) model and wavelet multi-resolution intelligent model for noise evaluation to improve the forecasting accuracy

Rainfall forecasting and approximation of its magnitude have a huge and imperative role in water management and runoff forecasting. The main objective of this paper is to obtain the relationship between rainfall time series achieved from wavelet transform (WT) and moving average (MA) in Klang River basin, Malaysia. For this purpose, the Haar and Dmey WTs were applied to decompose the rainfall time series into 7, 10 different resolution levels, respectively. Several preprocessing case studies based on 2-, 3-, 5-, 10-, 15-, 20-, 25-, and 30-month MAs were carried out to discover a longer-term trend compared to a shorter-term MA. The information and data were gathered from Klang Gates Dam, Malaysia, from 1997 to 2008. Regarding the behavior, the time series of 10-, 15-, 20-, and 30-day rainfall are decomposed into approximation and details coefficient with different kind of WT. Correlation coefficient R ² and root-mean-square error criteria are applied to examine the performance of the models. The results show that there are some similarities between MA filters and wavelet approximation sub-series filters due to noise elimination. Moreover, the results obtained that the high correlation with MAs can be achieved via Dmey WT compared to Haar wavelet for rainfall data. Moreover, clean signals could be used as model inputs to improve the model performance. Therefore, signal decomposition techniques for the purpose of data preprocessing could be favorable and could be appropriate for elimination of the errors.     

基于经验模式分解和极限学习机的铀资源价格预测方法

针对国际铀资源价格预测问题, 提出一种基于经验模式分解(EMD)、相空间重构(PSR) 和极限学习机(ELM) 的非线性组合预测方法. 首先通过EMD分解, 将原始价格序列分解为若干固有模态分量(IMF), 按频率高低将各IMF 分组叠加成3 个新序列; 然后在重构相空间的基础上构建不同的ELM模型, 分别对各IMF 序列进行预测; 最后对预测结果进行合成. 将该方法应用于实际铀资源价格预测, 并与径向基神经网络(RBF) 方法及单独ELM方法进行比较, 仿真结果表明该方法预测精度有明显的提高.

     

基于集合经验模式分解的火灾时间序列预测

采用集合经验模式分解(EEMD)和多变量相空间重构技术,结合非线性支持向量回归(SVR)模型,提出一种火灾次数时间序列组合预测方法。根据EEMD将非平稳的火灾时间序列分解为一系列不同尺度的固有模态分量,利用多变量相空间重构技术对分解的各个分量进行相空间重构,构建其训练数据,对重构的训练数据建立各分量的非线性支持向量回归预测模型,使用SVR集成预测方法对火灾时间序列进行预测。仿真结果表明,与单变量相空间重构方法以及SVR方法相比,该方法具有较高的预测精度。     

基于小波神经网络的混沌时间序列分析与相空间重构

探讨了小波神经网络在混沌时间序列分析与相空间重构中的应用,通过混沌时间序列单步预测与多步预测的例子,比较了小波神经网络与MLP的逼近和收敛性能,对最近提出的一种多分辨率学习策略进行了改进,利用连续3次样条小和正交Daubechies小波代替Haar小波对时间序列做小波分解;用改进的学习算法训练网络并应用到混沌序列相空间重构中,实验结果表明,小波神经网络比MLP和ARMA模型具有更强大的逼近能力,因而十分适合应用于时间序列分析中;多分辨率学习算法可作为分析复杂混沌时间序列的一种重要工具。     

改进双边滤波和阈值函数的图像增强算法

针对基于单尺度Retinex算法产生的图像泛灰现象和光晕现象、基于双边滤波Retinex算法的泛灰现象及噪声放大现象。提出基于小波变换的改进双边滤波的Retinex图像增强算法和改进阈值函数去噪算法。该方法对图像进行小波分解,获得图像的低频和高频系数;采用改进双边滤波的Retinex算法对图像低频系数进行处理,采用改进阈值函数对高频系数进行处理;采用离散小波反变换得到增强后的重构图像;对重构图像进行分段性线性变换,增强图像对比度。实验结果表明,该方法避免了图像泛灰和光晕现象,并有效去除了噪声,细节丰富,对比度强,为图像后续处理奠定基础。     

时间序列相似性定义延拓

时间序列相似性定义没有一个明确的、统一的表述方法,造成了研究上的困难。将研究序列分解为多个与参照序列等维的子序列,把问题转化为研究子序列与参照序列的相似性。选择满足保范同构的线性变换算子对子序列和参照序列进行变换,以降低直接计算的复杂度。利用集合理论对相似关系进行了宏观描述,用子序列与参照序列变换前后向量差的范数定义序列相似性度量函数,将相似性度量进行了统一。研究结果为基于傅立叶变换和小波变换研究时间序列的相似性提供了理论依据。     

基于小波包与长短时记忆融合的铁路旅客流量预测模型

短期铁路客运需求量的实时精准预测可以为实时调整客运服务结构提供依据.铁路旅客流量数据具有时变性、非线性和随机波动性等特点,传统的预测模型无法精准的预测短期内的客流量.本文提出一种基于小波包分解与长短时记忆融合的深度学习预测模型（WPA-LSTM）,首先用小波包分解将原始客运量时间序列分解重构成多个不同尺度的低频和高频序列,然后分别针对各个子序列进行LSTM模型训练和预测,最后将各子序列的预测值叠加作为WPA-LSTM模型的输出.采用某高铁367天的日旅客流量数据对模型进行实验验证,并与季节性模型和基于经验模态的长短时记忆融合模型进行对比,实验结果表明,WPA-LSTM模型可有效提高铁路旅客流量预测的精度.     

多重分支时间延迟神经网络的混沌预测研究

采用新型多重分支时间延迟神经网络进行混沌时间序列预测研究．在网络初始状态和实际系统初始状态不严格相等的情况下，探讨该网络对非线性系统的逼近能力．结合相空间重构理论确定网络结构，使网络能够包含有效的预测信息．文中采用Rossler混沌方程产生的混沌时间序列和实际观测的年太阳黑子时间序列作为实例．仿真表明本文所建网络可成功地应用于混沌系统的建模和预测，而且该方法可以达到较高的精度．