共查询到20条相似文献,搜索用时 156 毫秒
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相空间重构-最小二乘支持向量机用于间歇过程变量在线预报 总被引:1,自引:0,他引:1
时间序列预测技术可实现过程参数未来变化趋势的早期预报,从而为分析判断工况是否正常、确定转入下一工序的时机提供依据.针对间歇过程数据长度短、非线性、动态、不同批次数据不等长等特点,提出了一种基于相空间重构-最小二乘支持向量机的非线性时间序列预测方法.首先将多批次数据随机的拼接组成长数据向量,差分处理后采用相空间重构关联积分C-C方法计算该序列的延迟时间τ和嵌入维数m,从而构建训练集和检验集,然后采用最小二乘支持向量机算法建立预测模型.对某间歇蒸馏过程上升气温度建立的5步预测模型可用于生产现场的在线预报. 相似文献
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基于神经网络的炼油厂常压蒸馏350℃含量预测 总被引:1,自引:0,他引:1
常压塔四线350℃馏出含量是炼油厂常压蒸馏生产过程的重要质量指标,它与常压炉出口温度等多个变量之间存在严重的非线性关系,而且无法实时在线用仪表直接测量。论文提出了基于RBF神经网络的常四350℃含量预报模型,并用计算机软测量方式,对中石化广州分公司常压蒸馏装置(一)的实际数据进行模型验证研究。实验结果表明,该方法速度快,对实际生产具有指导意义。 相似文献
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《计算机与应用化学》2017,(1)
针对间歇过程的在线故障诊断需要预测过程变量的未知输出问题,提出了一种数据展开和故障分类器数据选择相结合的方法。首先,对包含批次信息的三维数据进行数据展开,对间歇过程的多阶段分别建立PCA模型并进行过程的故障监测;然后,选取故障发生时刻之后的部分长度采样时刻的数据进行故障的特征提取,离线建立LSSVM的故障分类器模型;最后,通过故障分类器进行在线故障诊断,实现故障分类并确定发生了某类故障。该方法提高了间歇过程在线故障诊断的实时性和准确性,通过青霉素发酵仿真过程的应用,进一步验证所提方法的可行性和有效性。 相似文献
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基于自组织神经网络的烧结终点自适应预报系统的开发 总被引:3,自引:0,他引:3
烧结终点的在线检测和提前预报对于稳定终点,进而提高烧结矿强度和产量、降低能耗有重要意义。文章介绍了烧结终点的软测量方法;提出了一个新的预报参数——风箱废气温度曲线拐点;将多层前向人工神经网络应用于烧结终点的预报,对BP算法做了较大改进,使学习算法可以自组织神经网络的结构。应用这些技术开发的烧结终点自适应预报系统能够快速、准确地判断和预报烧结终点的状态,可用于在线操作指导或作为自动控制的依据。 相似文献
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《计算机与应用化学》2015,(12)
乙烯裂解炉管在石化行业中至关重要,其炉管质量问题对整个化工过程安全生产也有很大影响,它由离心铸造过程获得。离心铸造过程属于间歇过程,但它在每个批次中也包含离散数据信息,而非时间序列。为了实现对炉管质量的监测,本文利用离心铸造过程中可以获得的参数,提取时间序列中的关键统计信息,形成每个批次的多变量样本信息,用主元分析方法建立炉管质量监测模型,所得到质量监测模型的目前正确率为80%,误报率为10%。通过贡献图的方法对预报结果进行了分析,剖析了引起炉管质量问题的原因,为工业炉管的正常生产提供了指导。 相似文献
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烧结终点预报对于提高烧结矿强度和产量、降低能耗具有重要意义,但是烧结终点状态受多种因素影响,无法直接检测,只能由操作工依据经验进行判断,严重影响了烧结生产的稳定运行.本系统运用K均值聚类分析的样本优选方法对海量数据进行处理,选择具有代表性的样本,从而有效缩小样本空间、改善样本质量.使用风箱温度曲线计算废气温度上升点和烧结终点软测量值,以台车速度和点火温度作为输入,采用BP神经网络模型,对烧结终点位置进行预报.在实际应用中,该模型预报结果较准确地反映了烧结终点位置的变化,起到了稳定生产、节约能源的作用. 相似文献
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为了深入了解三角形螺旋填料旋转床间歇精馏不稳态开工过程的影响因素及规律,以乙醇-水为实验物系进行实验和模型研究。改变料液浓度、再沸器加热功率和转速的开工实验结果表明,旋转填料床中强大的离心力和高效填料的协同作用使得间歇精馏不稳态开工过程得到极大强化、开工时间很短(30min左右),节能;在最佳转速下开工可使馏出液浓度最大,馏出液浓度随料液浓度和再沸器加热功率的增加而增大。旋转填料床间歇精馏过程在最佳转速和较大的再沸器加热功率下开工,效果最好;以平衡级理论为基础建立的数学模型能较好地描述该过程的不稳态开工规律,模拟值与实验值吻合良好,平均相对误差在2%以内。旋转填料床是强化间歇精馏过程的有效途径。 相似文献
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Model reduction and optimization of reactive batch distillation based on the adaptive neuro-fuzzy inference system and differential evolution 总被引:1,自引:1,他引:0
This paper considers the application of the adaptive neuro-fuzzy inference system (ANFIS) instead of the highly nonlinear
model of a reactive batch distillation column for optimization. The architecture has been developed for fuzzy modeling that
learns information from a data set, in order to compute the membership function and rule base in accordance with the given
input–output data. In this work, the differential evolution algorithm has been employed for optimization of operation policy
of reactive batch distillation for producing ethyl acetate. In optimization, minimal batch time and high purity of product
are considered, and reflux ratio and final batch time are taken as decision parameters. The results show that the reduced
model (ANFIS) is able to properly create a robust model of the reactive batch distillation, and CPU use is reduced to 1/18,000
of that of a real mathematical model. The highest yield and mole fraction of ethyl acetate were achieved through the use of
the obtained optimization policy. 相似文献
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智慧农业是实现农业精准化的技术解决方案,智慧农业系统可以实时监测植物生长的各类环境参数,并可以应用相应的预测模型来模拟农作物生长环境的变化趋势,为科学决策提供依据。近年来有很多学者提出了时间序列的预测模型算法,在预测稳定性方面取得了不错的效果。为了进一步提升时间序列的预测精度,提出一种基于差分整合移动平均自回归模型和小波神经网络的组合预测模型。该组合模型结合2个单项模型优点,用差分整合移动平均自回归模型来拟合序列的线性部分,用小波神经网络来校正其残差,使其拟合曲线更接近于实际值,采用温室内的历史温度数据来验证该组合模型的精确度,最后将组合模型与传统预测模型的预测结果进行对比。结果表明,该组合模型用于温室温度预测的精确度更高,拟合效果更好,相比于传统模型预测算法计算效能提高了20%左右。 相似文献
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基于神经网络的非线性时间序列故障预报 总被引:4,自引:0,他引:4
对模型未知非线性系统, 将系统输出组成时间序列并通过空间嵌入的方法转化为一个离散动态系统. 利用线性 AR 模型拟合时间序列的线性部分, 用神经网络拟合时间序列的非线性部分并补偿外界未知的扰动, 提出了通过对状态的观测实现时间序列一步预测的方法. 利用滚动优化的思想将一步预测推广, 提出了时间序列的 N 步预测方法, 证明了时间序列预测误差有界. 通过对预测误差进行概率密度估计和检验, 提出了故障的预报方法. 对 F-16 歼击机的结构故障预报结果表明了方法的有效性. 相似文献
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Forecasting financial time series using a methodology based on autoregressive integrated moving average and Taylor expansion 下载免费PDF全文
Financial time series prediction is regarded as one of the most challenging job because of its inherent complexity, and the hybrid forecasting model incorporating autoregressive integrated moving average and support vector machine (SVM) has been implemented widely to deal with the both linear and nonlinear patterns in time series data. However, the SVM model does not take into consideration the time correlation knowledge between different data points in time series, which impacts the learning efficiency of the SVM in real application. To overcome this restriction, this paper proposes the Taylor Expansion Forecasting model as an alternative to the SVM and develops a novel hybrid methodology via combining autoregressive integrated moving average and Taylor Expansion Forecasting to exploit the comprehensive forecasting capacity to the financial time series data with noise. Both theoretical proof and empirical results obtained on several commodity future prices demonstrate that the proposed hybrid model improves greatly the forecasting accuracy. 相似文献
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Masahiro Ohshima Hiromu Ohno Iori Hashimoto Mikiro Sasajima Masayuki Maejima Keiichi Tsuto Tadaharu Ogawa 《Journal of Process Control》1995,5(1)
This paper describes the results of a joint university-industry study to control a fatty acid distillation sequence, which is plagued with severe disturbance problems. In order to solve the disturbance problem, a model predictive control algorithm is modified in terms of disturbance prediction. Assuming that the dynamics of the unmeasured disturbances is generated by an auto-regressive form, the dynamics of the disturbance can be adaptively identified by using time series data of prediction errors and inputs. Using an identified disturbance model with a process model, future outputs are predicted. Control actions are determined so that the predicted output is as close to the target value as possible. This modified model predictive control aglorithm is applied to a ratio control scheme for three distillation columns. The control system developed has been in use sucessfully for more than six years to produce commercial products. 相似文献
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利用Visual Basic 6.0语言开发间歇精馏常规设计及优化设计软件。软件可用于不同物系(二元理想及非理想溶液),采用不同操作方式(恒馏出液组成操作和恒回流比操作)间歇精馏的常规设计和优化设计(包括单变量优化和多变量优化)计算。软件采用面向对象的编程技术,对精馏组分的物性参数、汽液相平衡数据实行数据库操作,界面友好,使用方便。 相似文献
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为解决复杂系统中非线性时间序列预测模型构建效率低、预测精度低的问题,提出基于组合模型的HURST-EMD预测算法.采用EMD算法将非线性时间序列分解为代表原始序列特征的各个IMF,然后引入赫斯特(Hurst)指数将同类的IMF整合为新的分量,最后选用LS-SVR-ARIMA模型进行组合预测.在该算法中,设计了序列分类整合等过程,优化了建模的计算量,构建了高效精准的预测模型.为验证模型的有效性,采用上证指数公共数据集和真实交通流数据进行检验,实验结果表明,改进的基于组合模型的HURST-EMD预测算法在提高预测效率的同时具有更好的预测精度. 相似文献
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Lemma D. Tufa M. Ramasamy Sachin C. Patwardhan M. Shuhaimi 《Journal of Process Control》2010,20(1):108-120
A unified scheme for developing Box–Jenkins (BJ) type models from input–output plant data by combining orthonormal basis filter (OBF) model and conventional time series models, and the procedure for the corresponding multi-step-ahead prediction are presented. The models have a deterministic part that has an OBF structure and an explicit stochastic part which has either an AR or an ARMA structure. The proposed models combine all the advantages of an OBF model over conventional linear models together with an explicit noise model. The parameters of the OBF–AR model are easily estimated by linear least square method. The OBF–ARMA model structure leads to a pseudo-linear regression where the parameters can be easily estimated using either a two-step linear least square method or an extended least square method. Models for MIMO systems are easily developed using multiple MISO models. The advantages of the proposed models over BJ models are: parameters can be easily and accurately determined without involving nonlinear optimization; a prior knowledge of time delays is not required; and the identification and prediction schemes can be easily extended to MIMO systems. The proposed methods are illustrated with two SISO simulation case studies and one MIMO, real plant pilot-scale distillation column. 相似文献