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风速时间序列具有非线性和非平稳性的特点,传统的预测方法难以建立风速间的函数关系,因此风速时间序列的预测结果精度不高。人工神经网络所具有的强非线性拟合能力有效地解决了风速时间序列难以预测的痛点,文章选择Elman神经网络预测全国3个地区不同尺度的风速时间序列,初步探讨了神经网络风速预测的可行性。结果表明,Elman神经网络经过训练,具有时序非线性拟合的能力,但预测结果精度尚未提高。 相似文献
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基于相空间重构的非线性预测思想,建立一个时滞的BP神经网络模型,采用贝叶斯正则化方法提高BP网络的泛化能力,区别于一般的预测方法,非线性预测不仅注重数据拟合和精度改进,而且能够反映被预测系统的非线性特征。将该模型应用于某电子行业进出口贸易非线性时间序列的预测,结果证明改进的模型具有较好的泛化能力,准确拟合了进出口贸易发展的历史值和趋势。并在分析模型预测精度的同时,通过计算拟合序列和原序列的非线性特征量进行模型评价,证实预测模型能够合理地“捕捉”到产生原序列的非线性系统的动力学特征。 相似文献
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神经网络在时间序列预测中的应用研究 总被引:3,自引:0,他引:3
介绍了时间序列预测的基本概念、各种模型,分析了基于神经网络的时间序列预测方法,阐述了BP神经网络基本原理,提出了一种基于BP神经网络的时间序列的预测和方法。通过应用实例的分析表明,以此方法得到BP网络应用于非线性时间序列预测是可行的,神经网络方法可以成功地用于分析预测时间序列变量。 相似文献
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本文从信息论的角度出发,讨论了利用神经网络理论构造时间序列预测模型的可能性和关键问题,并在此基础上提出3种时间序列神经网络预测方法,它们是:神经网络非线性时间序列模型、神经网络多维时间序列模型和神经网络组合预测模型,将上述模型应用于实例的结果表明,在非线性信息的处理能力和预测精度方面都有很大提高。进一步,对今后智能信息预测方法的发展方向进行了探讨,提出了智能信息预测系统的结构模型。 相似文献
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时间序列神经网络预测方法 总被引:4,自引:0,他引:4
本文从信息论的角度出发,讨论了利用神经网络理论构造时间序列预测模型的可能性和关键问题,并在此基础上提出3种时间序列神经网络预测方法,它们是:神经网络非线性时间序列模型,神经网络多维时间序列模型和神经网络组合预测模型。将上述模型应用于实例的结果表明,在非线性信息的处理能力和预测精度方面都有很大提高。进一步,对今后智能信息预测方法的发展方向进行了探讨,提出了智能信息预测系统的结构模型。 相似文献
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基于复杂非线性系统的相空间重构理论和神经网络本质为非线性映射关系的特点,提出利用混沌时间序列重构相空间和BP神经网络构建其预测模型的方法。利用该方法对典型的Lorenz混沌时间序列进行了空间重构,研究了预测模型的预测效果,结果表明单步预测效果理想,多步预测在50步以内也能取得较小的预测误差,证明了混沌信号不同于随机噪声,具有短期可预测、长期不可预测的特征。该方法为具有混沌特性的时间序列如心电信号、电力负荷等预测模型的建立提供了理论基础。 相似文献
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针对高校涉密项目风险因素多和保密环境复杂的特点,利用三层BP神经网络对能够逼近任意非线性函数的良好特性,突破传统上基于统计学方法进行预测的限制,综合了时间序列的计算简单,需要历史数据少的优点,设计了一种体现时序的多因素动态时间序列BP神经网络预测模型,并将模型运用于某高校涉密项目泄密风险的预测研究中。仿真实验表明,此方法切实可行,而且具有较好的预测精度。 相似文献
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An analysis of nonlinear time series prediction schemes, realised though advanced Recurrent Neural Network (RNN) techniques is provided. Due to practical constraints in using common RNNs, such as the problem of vanishing gradient, some other ways to improve RNN based prediction are analysed. This is undertaken for a simple RNN through to the Pipelined Recurrent Neural Network (PRNN), which consists of a number of nested small-scale RNNs. A Nonlinear AutoRegressive Moving Average (NARMA) nonlinear model is introduced in the context of RNN architectures, and an posteriori mode of operation within that framework. Moreover, it is shown that the basic a priori PRNN structure exhibits certain a posteriori features. The PRNN based predictor, is shown to exhibit nesting, and to be able to represent block cascaded stochastic models, such as the Wiener–Hammerstein model. Simulations undertaken on a speech signal support the analysis. 相似文献
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Prasannavenkatesan Theerthagiri Menakadevi Thangavelu 《International Journal of Communication Systems》2019,32(9)
In this paper, we propose a speed prediction model using auto‐regressive integrated moving average (ARIMA) and neural networks for estimating the futuristic speed of the nodes in mobile ad hoc networks (MANETs). The speed prediction promotes the route discovery process for the selection of moderate mobility nodes to provide reliable routing. The ARIMA is a time‐series forecasting approach, which uses autocorrelations to predict the future speed of nodes. In the paper, the ARIMA model and recurrent neural network (RNN) trains the random waypoint mobility (RWM) dataset to forecast the mobility of the nodes. The proposed ARIMA model designs the prediction models through varying the delay terms and changing the numbers of hidden neuron in RNN. The Akaike information criterion (AIC), Bayesian information criterion (BIC), auto‐correlation function (ACF), and partial auto‐correlation function (PACF) parameters evaluate the predicted mobility dataset to estimate the model quality and reliability. The different scenarios of changing node speed evaluate the performance of prediction models. Performance results indicate that the ARIMA forecasted speed values almost match with the RWM observed speed values than RNN values. The graphs exhibit that the ARIMA predicted mobility values have lower error metrics such as mean square error (MSE), root MSE (RMSE), and mean absolute error (MAE) than RNN predictions. It yields higher futuristic speed prediction precision rate of 17% to 24% throughout the time series as compared with RNN. Further, the proposed model extensively compares with the existing works. 相似文献
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大气PM2.5浓度是一种具有较强时序特征的数据,故目前关于PM2.5浓度的预测多选择RNN、LSTM等序列模型进行.但由于RNN、LSTM等模型对不同时刻输入的数据都采用相同的权重进行计算,不符合类脑设计,造成PM2.5浓度预报准确率较低.针对以上问题,提出一种基于Adam注意力机制的PM2.5预测方法(AT-RNN和... 相似文献
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海杂波的短时非线性预测研究 总被引:2,自引:0,他引:2
海杂波预测是雷达信号处理和目标检测的研究热点。在海杂波具有混沌特性和非线性非平稳特点的基础上,研究了基于归一化RBF神经网络和最小二乘支持向量机(LSSVM)两种方法对海杂波时间序列进行非线性预测。考虑到海杂波是来自于移动海面的回波,预测应该考虑空间信息,因此提出一种基于LSSVM-耦合映像格子(CML)的海杂波时空预测,这样预测更具有物理意义。以实测海杂波数据作为预测的初始数据和预测效果比对,采用均方差和最大绝对误差作为预测效果评价标准。实验结果表明,由于LSSVM-CMI,算法考虑了海杂坡的时空信息,预测效果最优。 相似文献
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A nonlinear predictive model of speech, based on the method of time delay reconstruction, is presented and approximated using a fully connected recurrent neural network (RNN) followed by a linear combiner. This novel combination of the well established approaches for speech analysis and synthesis is compared with traditional techniques within a unified framework to illustrate the advantages of using an RNN. Extensive simulations are carried out to justify the expectations. Specifically, the network's robustness to the selection of reconstruction parameters, the embedding time delay and dimension, is intuitively discussed and experimentally verified. In all cases, the proposed network was found to be a good solution for both prediction and synthesis 相似文献
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Yonghua Fang Yagoub M.C.E. Fang Wang Qi-Jun Zhang 《Microwave Theory and Techniques》2000,48(12):2335-2344
A new macromodeling approach is developed in which a recurrent neural network (RNN) is trained to learn the dynamic responses of nonlinear microwave circuits. Input and output waveforms of the original circuit are used as training data. A training algorithm based on backpropagation through time is developed. Once trained, the RNN macromodel provides fast prediction of the full analog behavior of the original circuit, which can be useful for high-level simulation and optimization. Three practical examples of macromodeling a power amplifier, mixer, and MOSFET are used to demonstrate the validity of the proposed macromodeling approach 相似文献
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Yuanjin Zheng Zhiping Lin David B. H. Tay 《Circuits, Systems, and Signal Processing》2001,20(5):575-597
In a recent companion paper, a new method has been presented for modeling general vector nonstationary and nonlinear processes based on a state-dependent vector hybrid linear and nonlinear autoregressive moving average (SVH-ARMA) model. This paper discusses some potential applications of the SVH-ARMA model, including signal filtering, time series prediction, and system control. First, a state-space model governed by a hidden Markov Chain is shown to be equivalent to the SVH-ARMA model. Based on this state-space model, the extended Kalman filtering and Bayesian estimation techniques are applied for noisy signal enhancement. The result of a noisy image enhancement verifies that the model can track the time-varying statistical characteristics of nonstationary and nonlinear processes adaptively. Second, the SVH-ARMA model is used for a vector time series prediction, which can attain more accurate multiple step ahead prediction, than conventional forecasting methods. Third, a new technique is developed for predicting scalar long correlation time series in the wavelet scale space domain based on the SVH-ARMA model. Dyadic wavelet transform is employed to convert a scalar time series to a vector time series, to which the SVH-ARMA model is applied for vector time series prediction. More accurate and robust forecasting results in both one step and multiple step ahead prediction can be gained. See also the companion paper on theory, by Zheng et al., pp. 551–574, this issue. 相似文献
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Mandic D.P. Chambers J.A. 《Vision, Image and Signal Processing, IEE Proceedings -》1998,145(6):365-370
Recurrent neural networks (RNNs) are well established for the nonlinear and nonstationary signal prediction paradigm. Appropriate learning algorithms, such as the real-time recurrent learning (RTRL) algorithm, have been developed for that purpose. However, little is known about the RNN time-management policy. Here, insight is provided into the time-management of the RNN, and an a posteriori approach to the RNN based nonlinear signal prediction paradigm is offered. Based upon the chosen time-management policy, algorithms are developed, from the a priori learning-a priori error strategy through to the a posteriori learning-a posteriori error strategy. Compared with the a priori algorithms, the a posteriori algorithms offered are shown to provide a better prediction performance with little further expense in terms of computational complexity. Simulations undertaken on speech using the newly introduced algorithms confirm the theoretical results 相似文献