软测量模型的建立

软件测量是采用过程中比较容易测量的辅助变量，构造推断估计器来推算出难以测量或根本无法测量的关键工艺参数；是根据某种最优准则，选择一组既与主导变量有密切联系又容易测量的变量，即辅助变量，通过构造某种数学关系，用计算机软件实现对主导变量的在线估计，介绍了软测量的核心技术，并重点阐述了软测量模型建立的方法，还给出了建立一个完整的软测量模型的步骤。     

空分过程变量预测的多变量时间序列分析方法

氮塞是空分过程的常见故障,粗氩塔冷凝器出口氩气含氩量是工业现场中指示氮塞是否发生的关键变量,对该变量进行准确的预测可以使氮塞故障的报警时间提前.本文采用多变量时间序列相空间重构的方法,建立了粗氩塔冷凝器出口氩气含氩量和其它过程变量之间的一步线性回归预测模型,以迭代方式获得多步预测的结果,并利用滑动窗口实现了模型参数的在线修正.通过某钢铁公司空分装置实际数据的建模与仿真,分析了相空间重构时嵌入维数以及预测步数的选取对最终预测结果的影响,即预测均方误差与嵌入维数成反比,与预测步数成正比.仿真结果同时表明,本文建立的模型能够较为准确地对空分过程关键变量进行预测,预测提前时间在4～5分钟之间.     

一种基于支持向量机的软测量建模方法

在生产过程中,在线分析仪表通常被用于对被测介质的组成或物性参数进行自动连续测量,但很多参数无法通过在线分析仪表直接测量获得。在工业现场,通常采用软测量技术来弥补在线分析仪表的不足。软测量技术也称软仪表技术,是基于推断控制理论的一门新兴工业技术。其利用易测过程变量与难以直接测量的待测过程变量之间的数学关系,通过各种计算和估计方法,实现对待测过程变量的测量。为了提高软测量模型的性能,提出一种基于支持向量机的软测量建模方法。该模型结构分为两层:一层用于分析工业数据在时间序列上的相互关系,解决时间序列的相关性问题;一层用于软测量建模和分析,解决非线性回归模型的鲁棒性。仿真结果表明,该软测量建模方法在进行在线预测时具有很好的性能,为软测量技术在工业现场的应用提供了一种方法。     

基于NARX神经网络的油品性质软测量建模

软测量技术是石化生产过程中在线监测油品难测性质的重要手段。本文提出了一种基于NARX神经网络的软测量仪表用于原油蒸馏装置中油品关键性质的在线预测。首先,利用流程模拟软件建立了原油蒸馏过程的动态模型。然后,基于动态模型的阶跃实验数据,建立了以装置操作变量为输入、油品关键性质为输出的NARX神经网络预测模型,并提出能有效减少模型预测误差的修正方法。仿真实验结果表明,所提出的误差修正方法可明显减少预测结果中的"大误差点",降低根均方误差,因此,所建立的软测量仪表可用于油品关键性质的在线预测。     

WSN流量的双卡尔曼并行递推预测算法研究

针对WSN流量预测,基于AR模型提出一种WSN流量双卡尔曼并行递推预测算法.该算法使用两个Kalman滤波器,交替进行AR模型参数的递推辨识与时变数据中真实值的最优估计,根据序列数据的最新信息实时修正AR模型参数进行动态预测.同时针对大步长的流量预测,引入滚动修正思想,克服动态预测算法存在间隔时间过长的缺点,降低多步预测误差.实验研究表明,利用研究的双卡尔曼并行递推算法使用AR模型进行多步预测,从原理设计和实现算法上,实现了WSN流量的准确预测.     

在线场景更新的多阶段非线性模型预测聚合反应控制

针对聚合过程中时不变不确定性参数不能直接估计的情况,导致的多阶段非线性模型预测控制中场景树生成的合理性问题,提出一种基于贝叶斯概率加权的在线场景更新算法.该方法利用前一时间步中每个场景的模型预测信息和过程状态测量信息计算对应场景的概率权重,然后通过合适的自适应步长在线更新场景树中不确定性的离散实现场景.所提方法在保证过程约束满足的同时,逐渐缩小不确定性集合逼近不确定性的真实值,从而降低保守性,提升控制器性能.通过多个批次的半间歇聚合反应过程实例仿真结果表明,所提出的方法可以有效降低批次反应时间,提高生产效率.     

基于DP-RFR的多模型软测量建模

实际工业过程往往是一个多工况、非线性的大规模复杂系统,使得单一模型软测量建模方法难以充分挖掘数据信息。针对这一问题提出了一种基于密度峰(Density Peak,DP)聚类和随机森林回归(Random Forest Regression,RFR)的多模型软测量建模方法,从而对主导变量进行估计。首先,利用DP聚类算法对训练数据进行划分;其次,采用RFR方法建立各子类的回归子模型;最后采用开关切换的方法进行多模型融合。将提出方法应用于TE过程和丁烷蒸馏过程的软测量建模中,分别对产物G含量和丙烷含量进行估计。仿真结果表明估计精度得到提高,证明该方法是有效的。     

工业软测量模型结构与输入变量选择的研究

针对过程工业中难以直接测量的变量建立其软测量模型,对于实现关键指标的在线监测和实时控制具有十分重要的意义.变量的选择直接关系到神经网络软测量模型的预测性能,针对现有输入变量和网络结构选择方法在工业应用中的不足,提出了一种基于敏感度分析的方法来确定网络输入变量集和前馈神经网络隐含层节点个数,并建立了高密度聚乙烯(HDPE...     

基于小波和LS-SVM的软测量建模方法

针对工业过程中某些重要过程变量难以实现在线检测的问题,提出了一种基于小波和最小二乘支持向量机(LS-SVM)的软测量建模方法.首先通过小波变换把样本数据序列分解为不同频段的子序列,然后对这些子序列分别采用LS-SVM进行建模,最后通过小波重构得到主导变量的估计值.其中采用量子粒子群算法(PSO)来优化选取LS-SVM参数.通过仿真实验验证此方法,实验结果表明所提出的方法具有估计精度高、泛化能力强等优点.     

基于邻近相点聚类分析的多变量局域多步预测*

针对高维混沌复杂系统的多步预测问题,提出了一种基于邻近相点聚类分析的多变量局域多步预测模型。首先对于多变量邻近相点的选取,结合邻近相点多步回溯后的演化规律和变量间的关联信息对演化轨迹的影响,提出了一种新的多变量演化轨迹相似度综合判据;然后针对选取全局最优邻近相点耗时长的缺点,提出了一种基于邻近相点聚类分析的新方案,来降低多步预测时间,提高预测效率。最后通过Lorenz混沌数据仿真实验,实验结果表明该模型具有优良的预测性能。     

Chlorophyll-a Predicting Model Based on Dynamic Neural Network

Algal blooms are one of the most prevalent global problems. Studying the Chlorophyll-a (Chl-a) predicting model helps to control algal blooms. Predicting the behavior of algae is difficult because of the complex physical, chemical, and biological processes involved. Artificial neural network (ANN) models have been determined to be useful and efficient, especially for such problems for which the characteristics of the processes are difficult to describe using numerical models. An indoor simulated environment is designed for algal cultivation to analyze the temporal change in the algae biomass of Taihu Lake during summer. A Chl-a prediction model based on a nonlinear autoregressive neural network with exogenous inputs (NARX) that can detect and consider within the time dependency is proposed. The NARX model is compared to a static neural network and a dynamic neural network: feedforward neural network (FNN) and Elman recurrent neural network (ERNN). The performance of the proposed NARX model was examined with experimental data collected over 3 months in 2010. The results showed that the NARX model outperformed the other ANN models and significantly enhance the accuracy of Chl-a prediction.     

集成自编码与PCA的高炉多元铁水质量随机权神经网络建模

针对随机权神经网络（Random vector functional-link networks，RVFLNs）建模存在的过拟合和泛化能力差的问题，集成自编码（Autoencoder）和主成分分析（Principal component analysis，PCA）技术，提出一种新型的改进RVFLNs算法，即AE-P-RVFLNs算法，用于建立高炉多元铁水质量在线估计的NARX（Nonlinear autoregressive exogenous）模型.首先，为了尽可能挖掘实际复杂工业数据中的有用信息和充分揭示输入数据之间的内在关系，采用Autoencoder前馈随机网络技术训练建模输入数据，并将训练得到的输出权值作为后续RVFLNs的输入权值；然后，引入PCA技术对RVFLNs的高维隐层输出矩阵进行降维，避免隐层输出矩阵多重共线性问题，从而解决由于隐层节点过多导致模型过拟合的问题；最后，基于所提AE-P-RVFLNs算法建立某大型高炉多元铁水质量在线估计的NARX模型.工业实验和比较分析表明:采用本文算法建立的多元铁水质量在线估计模型可有效提高运算效率和估计精度，尤其是避免常规RVFLNs建模存在的过拟合问题.     

EMD与NARX神经网络的风电场总功率组合预测

探索构建对风电场总功率进行直接预测的高精度组合预测算法。考虑到风速的非平稳性导致风电总功率表现为非平稳时间序列,采用NARX神经网络作为初步预测模型,提出了经验模态分解与NARX神经网络相结合的混合预测模型。对风电场总功率非平稳时间序列进行经验模态分解,得到不同频带本征模式分量的平稳序列。对不同频带的平稳分量建立相应的NARX神经网络预测模型,并将各分量模型的预测值进行等权求和得到最终预测值。此外,为研究不同时间间隔对预测结果的影响,采用某大型风电场时间间隔为5 min与15 min的数据进行实验。预测结果表明,提出的组合预测模型适合于总功率预测,其预测效果比传统模型的效果更佳,且时间间隔为5 min的数据比时间间隔为15 min的数据预测精度更高。     

Hybrid control of a pneumatic artificial muscle (PAM) robot arm using an inverse NARX fuzzy model

We investigated the possibility of applying a hybrid feed-forward inverse nonlinear autoregressive with exogenous input (NARX) fuzzy model-PID controller to a nonlinear pneumatic artificial muscle (PAM) robot arm to improve its joint angle position output performance. The proposed hybrid inverse NARX fuzzy-PID controller is implemented to control a PAM robot arm that is subjected to nonlinear systematic features and load variations in real time. First the inverse NARX fuzzy model is modeled and identified by a modified genetic algorithm (MGA) based on input/output training data gathered experimentally from the PAM system. Second the performance of the optimized inverse NARX fuzzy model is experimentally demonstrated in a novel hybrid inverse NARX fuzzy-PID position controller of the PAM robot arm. The results of these experiments demonstrate the feasibility and benefits of the proposed control approach compared to traditional PID control strategies. Consequently, the good performance of the MGA-based inverse NARX fuzzy model in the proposed hybrid inverse NARX fuzzy-PID position control of the PAM robot arm is demonstrated. These results are also applied to model and to control other highly nonlinear systems.     

Link prediction in evolving heterogeneous networks using the NARX neural networks

In this article, we propose a novel multivariate method for link prediction in evolving heterogeneous networks using a Nonlinear Autoregressive Neural Network with External Inputs (NARX). The proposed method combines (1) correlations between different link types; (2) the effects of different topological local and global similarity measures in different time periods; (3) nonlinear temporal evolution information; (4) the effects of the creation, preservation or removal of the links between the node pairs in consecutive time periods. We evaluate the performance of link prediction in terms of different AUC measures. Experiments on real networks demonstrate that the proposed multivariate method using NARX outperforms the previous temporal methods using univariate time series in different test cases.     

Wind speed forecasting using the NARX model,case: La Mata,Oaxaca, México

This study presents the generation of a nonlinear autoregressive exogenous model (NARX) for wind speed forecasting in a 1 h, in advance horizon. A sample of meteorological data of hourly measurements taken during a year was used for the generation of the model. The variables measured were as follows: wind speed, wind direction, solar radiation, pressure, and temperature. All measurements were taken by the Comision Federal de Electricidad (CFE) at La Mata, in the state of Oaxaca, Mexico. Using the Mahalanobis distance, the sample of data was treated in order to detect deviated values in multivariable samples. Later on, the statistical Granger test was conducted to establish the entry variables that would be incorporated into the model. Since solar radiation was the only one determined as the cause for wind speed, it was the variable used in the configuration of the model. To compare the NARX model, a one-variable, nonlinear autoregressive model (NAR) was also generated. Both models, the NARX and the NAR were compared against the persistence model by means of applying the statistical error forecast measurements of mean absolute error, mean squared error, and mean absolute percentage error to the test data. The results showed the NARX model as the most precise of the three, reflecting the importance of the inclusion of additional meteorological variables in the wind speed forecasting models.     

Forecasting peak air pollution levels using NARX models

Air pollution has a negative impact on human health. For this reason, it is important to correctly forecast over-threshold events to give timely warnings to the population. Nonlinear models of the nonlinear autoregressive with exogenous variable (NARX) class have been extensively used to forecast air pollution time series, mainly using artificial neural networks (NNs) to model the nonlinearities. This work discusses the possible advantages of using polynomial NARX instead, in combination with suitable model structure selection methods. Furthermore, a suitably weighted mean square error (MSE) (one-step-ahead prediction) cost function is used in the identification/learning process to enhance the model performance in peak estimation, which is the final purpose of this application. The proposed approach is applied to ground-level ozone concentration time series. An extended simulation analysis is provided to compare the two classes of models on a selected case study (Milan metropolitan area) and to investigate the effect of different weighting functions in the identification performance index. Results show that polynomial NARX are able to correctly reconstruct ozone concentrations, with performances similar to NN-based NARX models, but providing additional information, as, e.g., the best set of regressors to describe the studied phenomena. The simulation analysis also demonstrates the potential benefits of using the weighted cost function, especially in increasing the reliability in peak estimation.     

基于ZigBee的智慧教室节能系统研究

针对高校教室电能浪费问题,设计一种基于ZigBee的智慧教室节能系统;系统以STM32作为主控单元,由ZigBee协调器和终端节点形成的局域网络构成感知执行层,并通过Wi-Fi网络和云平台,实现控制中心对教室用电设备的状态感知和智慧控制;同时针对温控设备功率有限以及室内复杂的温度干扰环境而导致室内温度无法稳定保持在合适范围,基于KNN算法构建NARX神经网络温度预测模型,实现利用已有环境数据预测下一时刻温度,以达到对教室内温度的准确控制;最后,对系统功能和改进预测算法进行了实验验证,运行结果表明该系统可以稳定运行,并可以达到精确控制用电设备实现节约电能的目的。     

A Note on the Equivalence of NARX and RNN

This paper presents several aspects with regards the application of the NARX model and Recurrent Neural Network (RNN) model in system identification and control. We show that every RNN can be transformed to a first order NARX model, and vice versa, under the condition that the neuron transfer function is similar to the NARX transfer function. If the neuron transfer function is piecewise linear, that is f(x):=x if uxu , 1 and f(x):=sign(x) otherwise, we further show that every NARX model of order larger than one can be transformed into a RNN. According to these equivalence results, there are three advantages from which we can benefit: (i) if the output dimension of a NARX model is larger than the number of its hidden unit, training an equivalent RNN will be faster, i.e. the equivalent RNN is trained instead of the NARX model. Once the training is finished, the RNN is transformed back to an equivalent NARX model. On the other hand, (ii) if the output dimension of a RNN model is less than the number of its hidden units, the training of a RNN can be speeded up by using a similar method; (iii) the RNN pruning can be accomplished in a much simpler way, i.e. the equivalent NARX model is pruned instead of the RNN. After pruning, the NARX model is transformed back to the equivalent RNN.