基于L2正则化LSTM的非线性动态系统辨识

LSTM本身具有良好的非线性逼近能力,但在应用于化工流程工业建模时,存在模型泛化能力低的问题。对此,提出一种基于L2正则化LSTM的非线性动态系统辨识策略,将L2正则化项引入网络的损失函数中,优化网络结构,从而提高模型泛化能力。同时,利用TE过程进行相关验证实验,仿真结果表明:所提出的方法优于传统的BP神经网络和支持向量回归,能够有效地提高模型的精度和泛化能力,降低对辨识输入数据的要求。     

基于Hammerstein模型的非线性系统辨识方法研究

工业大系统中Hammerstein模型的非线性系统,一般都是多输入多输出系统,具有大滞后、大惯性、时变性和强耦合性的特点,它的数学模型难于精确获得;且传统PID控制器无法使控制效果处于最佳状态的局限性.为了更加快速准确控制,使系统更加地稳定工作在最佳工作状态.利用分散辨识方法对Hammerstein模型的非线性系统进行...     

基于CFD的系统辨识方法

概述了计算流体力学(CFD)数值模拟和系统辨识的原理,详细阐述了基于CFD数值模拟的系统辨识“灰箱”建模方法的基本原理和实现步骤,对近几年该方法的应用案例进行分析,指出该建模方法存在的问题及发展方向.     

基于自适应模糊推理的非线性系统辨识器设计

针对传统模糊建模方法中模型参数都是根据经验选取的局限性,提出一种类高斯隶属函数,推导了基于类高斯隶属函数的自适应模糊推理模型,利用Stone-Weierstrass定理证明了该模型能以任意精度逼近非线性系统.将自适应模糊推理模型应用于非线性动态系统辨识中,设计了非线性系统辨识器,采用梯度下降算法学习模型中参数,通过仿真得到了较好的辨识效果.     

基于系统输出分布的建模和控制方法及应用

提出了依据系统输出分布的特征变量选取方法,并提出了依据该项信息的建模和控制方法.仿真结果令人满意.在一个实验性二元精馏塔上用IBM-PC机进行试验亦获成功.简化模型能较好地近似精馏塔的逐板模型,控制策略优于常规PI控制策略.     

基于长短时记忆循环网络的塑料编织机故障诊断研究

长短时记忆(LSTM)循环神经网络的塑料编织机故障诊断法通过提取振动信号的能量矩,突出信号在时间轴上的分布特征,降低输入模型的向量维度。从多个特征向量构成的样本集中选择80%作为训练样本,训练LSTM循环神经网络模型,并利用剩余样本验证模型的检测精度;以准确率、查准率和查全率作为评价指标,利用多组不同的振动数据样本,对BP神经网络模型、卷积神经网络(CNN)模型和LSTM循环神经网络模型进行比较分析。结果表明:LSTM循环神经网络模型在不同样本中能够同时达到较高的准确率、查准率和查全率,其平均值分别可达95.69%、86.96%、96.89%,证明LSTM循环神经网络能充分学习具有时序特性的故障信息,对塑料编织机的故障诊断具有可行性和有效性。     

一种非线性系统在线辨识的选择性递推方法

针对非线性系统的在线辨识, 提出了一种选择性递推岭参数极限学习机方法。首先, 推导了岭参数极限学习机模型节点增加的递推算法, 以有效地更新在线模型。其次, 结合训练模型的相对误差, 提出模型节点递推增加的选择性策略, 以限制模型的复杂度, 获得更简单的递推辨识模型。通过一个典型非线性化工过程的在线辨识, 从多方面比较验证了所提出方法的简单有效, 更适合非线性过程的在线辨识。     

换热器网络同步最优综合方法──改进的MINLP转运模型法

针对现有的换热器网络最优综合方法的局限性，采用分级超结构转运模型和物流吸、放热潜力的概念，提出了非等温混合线性约束的换热器网络同步最优综合混合整数非线性规划（MINLP）的改进模型．该模型不仅可以对公用工程费用、换热面积、换热设备台数及物流的匹配选择进行多目标同步优化，而且在线性约束的条件下消除了等温混合的不合理假设，只需求解一次MINLP问题就可得到包括分流情况在内的最优网络结构．算例表明，该模型优于以往的几种同步优化模型．     

基于泄漏积分型回声状态网络的软测量动态建模方法及应用

提出一种基于泄漏积分型回声状态网络（LiESN）的软测量动态建模方法,给出LiESN的岭回归离线学习算法与递推最小二乘（RLS）在线学习算法。通过引入正则化系数,岭回归离线学习算法可有效地控制输出权值的幅值,改善ESN的预测性能。RLS在线学习算法能适应大数据集的处理,满足过程建模实时性的需求。将基于LiESN的软测量方法分别用于预测脱丁烷塔底部丁烷组分的含量及计算硫回收装置中尾气的组成,实现对精炼厂相关产品质量的实时监控,并采用模型残差的四图分析对建模性能进行评价。在同等条件下,与基本的ESN网络以及支持向量机（SVM）等软测量建模方法进行了比较,结果表明,所提出的LiESN方法取得了很好的预测性能,计算精度满足工业生产的实际要求。     

基于渐进黑箱理论的多变量辨识方法

为克服传统单变量开环阶跃测试法测试时间长和误差大的缺点,提出一种基于渐进黑箱理论的多变量辨识方法.针对辨识的几个基本问题:测试信号的设计、模型结构的选择、模型阶次的判别和参数估计,进行了全新的设计.采用平移的方法,把一个周期较长的伪随机二进制序列平移若干次,从而得到若干个近似两两互不相关的伪随机二进制序列作为多变量测试信号.选取高ARX模型作为参数模型,并用输出误差(OE)模型进行降阶模型的参数估计,降阶模型的阶次由最小描述长度(MDL)准则来判别.实例仿真的结果表明,用该方法解决多变量辨识问题,能减少测试时间,降低测试期间对设备产生的干扰,辨识的结果也优于常规辨识法.     

Attention LSTM network identification method based on factory data

The control system of chemical enterprises is becoming more and more complex, and identifying the controlled object model is the primary task of automatic control and optimization design. In view of the problem that most chemical process identification experiments need to apply test signals to the process, which may lead to production interruption or safety accidents, a long short-term memory(LSTM) nonlinear dynamic model identification algorithm combined with attention mechanism is proposed to adapt to plant time series data with characteristics of high dimension, strong coupling and nonlinearity. Based on LSTM model, the algorithm considers the importance of the input variables to the target variables, pays more attention to the key features that affect the output results in the input sequence, and improves the generalization ability of the LSTM model. The LSTM network model based on the daily operation data of the plant can be used as the digital virtual device of the identified object, and the local linear model can be identified offline on the virtual device by using the designed test data. The identification experiments on Tennessee-Eastman (TE) process verify the effectiveness of this method.     

基于LSTM神经网络的流化床干燥器内生物质颗粒湿度预测

生物质作为一种储量丰富、环境友好且易于获取的可再生能源,日渐成为能源研究利用领域的热点。生物质湿度是影响生物质利用效率的关键因素,因此干燥是生物质利用之前的必要步骤。流化床由于其良好的传热传质特性,在干燥过程中得到了广泛的应用。为了实时监测生物质颗粒的干燥过程,利用弧形静电传感器阵列,结合用于时间序列建模的长短期记忆（LSTM）神经网络,实现了流化床干燥器内生物质颗粒湿度的预测。在实验室规模的流化床干燥器上进行了多工况实验获取训练和测试数据,通过模型参数优化确定了LSTM模型。通过与标准循环神经网络（RNN）模型的预测结果的对比表明,LSTM神经网络模型的平均相对误差较小,能够较为准确地预测流化床干燥器内生物质颗粒的湿度。     

Perturbation signal design for neural network based identification of multivariable nonlinear systems

The paper focuses on issues in experimental design for identification of nonlinear multivariable systems. Perturbation signal design is analyzed for a hybrid model structure consisting of linear and neural network structures. Input signals, designed to minimize the effects of nonlinearities during the linear model identification for the multivariable case, have been proposed and its properties have been theoretically established. The superiority of the proposed perturbation signal and the hybrid model has been demonstrated through extensive cross validations. The utility of the obtained models for control has also been proved through a case study involving MPC of a nonlinear multivariable neutralization plant.     

Subspace based model identification for missing data

This article addresses the problem of missing process data in data-driven dynamic modeling approaches. The key motivation is to avoid using imputation methods or deletion of key process information when identifying the model, and utilizing the rest of the information appropriately at the model building stage. To this end, a novel approach is developed that adapts nonlinear iterative partial least squares (NIPALS) algorithms from both partial least squares (PLS) and principle component analysis (PCA) for use in subspace identification. Note that the existing subspace identification approaches often utilize singular value decomposition (SVD) as part of the identification algorithm which is generally not robust to missing data. In contrast, the NIPALS algorithms used in this work leverage the inherent correlation structure of the identification matrices to minimize the impact of missing data values while generating an accurate system model. Furthermore, in computing the system matrices, the calculated scores from the latent variable methods are utilized as the states of the system. The efficacy of the proposed approach is shown via simulation of a nonlinear batch process example.     

基于长短时记忆神经网络和支持向量数据描述的间歇过程监测方法

间歇过程操作是化工过程中的一种重要生产方式.与连续过程不同,间歇生产不是在一个稳定的工作状态运行,而是根据设定的原料比例、操作条件所对应的操作轨迹运行.因此间歇过程数据具有多阶段性、动态时变性和非线性等特性,传统的监测方法难以应用于对间歇过程生产运行状态的监测.为了解决这个问题,提出了一种新的间歇过程监测策略.首先基于...     

基于自适应模糊神经网络的非线性系统模型预测控制

针对非线性动态系统的控制问题,提出了一种基于自适应模糊神经网络(adaptive fuzzy neural network,AFNN)的模型预测控制(model predictive control, MPC)方法。首先,在离线建模阶段,AFNN采用规则自分裂技术产生初始模糊规则,采用改进的自适应LM学习算法优化网络参数;然后,在实时控制过程,AFNN根据系统输出和预测输出之间的误差调整网络参数,从而为MPC提供一个精确的预测模型;进一步,AFNN-MPC利用带有自适应学习率的梯度下降寻优算法求解优化问题,在线获取非线性控制量,并将其作用到动态系统实施控制。此外,给出了AFNN-MPC的收敛性和稳定性证明,以保证其在实际工程中的成功应用。最后,利用数值仿真和双CSTR过程进行实验验证。结果表明,AFNN-MPC能够取得优越的控制性能。     

基于JIT-MOSVR的软测量方法及应用

针对传统多模型软测量方法在面对复杂、多变工况时缺少在线更新机制、更新时输出精度降低等问题,提出了一种基于即时学习算法（JIT）的多模型在线软测量方法（MOSVR）。离线阶段首先采用模糊C均值聚类（FCM）对训练数据进行聚类,接着采用SVR建立初始模型集。在线部分以多模型输出作为主要输出,当出现新工况时,通过在线模型更新策略（OSMU）将输出模式切换为JIT,同时多模型集进行在线更新。该方法不仅拥有多模型输出的快速性、精确性,而且在模型更新时通过JIT模式还能保证输出的连续性、稳定性、精确性。最后将该软测量方法进行了数值仿真并运用到乙烷浓度软测量中,验证了该方法的有效性。     

On identification of well‐conditioned nonlinear systems: Application to economic model predictive control of nonlinear processes

The focus of this work is on economic model predictive control (EMPC) that utilizes well‐conditioned polynomial nonlinear state‐space (PNLSS) models for processes with nonlinear dynamics. Specifically, the article initially addresses the development of a nonlinear system identification technique for a broad class of nonlinear processes which leads to the construction of PNLSS dynamic models which are well‐conditioned over a broad region of process operation in the sense that they can be correctly integrated in real‐time using explicit numerical integration methods via time steps that are significantly larger than the ones required by nonlinear state‐space models identified via existing techniques. Working within the framework of PNLSS models, additional constraints are imposed in the identification procedure to ensure well‐conditioning of the identified nonlinear dynamic models. This development is key because it enables the design of Lyapunov‐based EMPC (LEMPC) systems for nonlinear processes using the well‐conditioned nonlinear models that can be readily implemented in real‐time as the computational burden required to compute the control actions within the process sampling period is reduced. A stability analysis for this LEMPC design is provided that guarantees closed‐loop stability of a process under certain conditions when an LEMPC based on a nonlinear empirical model is used. Finally, a classical chemical reactor example demonstrates both the system identification and LEMPC design techniques, and the significant advantages in terms of computation time reduction in LEMPC calculations when using the nonlinear empirical model. © 2015 American Institute of Chemical Engineers AIChE J, 61: 3353–3373, 2015     

Error‐triggered on‐line model identification for model‐based feedback control

In industry, it may be difficult in many applications to obtain a first‐principles model of the process, in which case a linear empirical model constructed using process data may be used in the design of a feedback controller. However, linear empirical models may not capture the nonlinear dynamics over a wide region of state‐space and may also perform poorly when significant plant variations and disturbances occur. In the present work, an error‐triggered on‐line model identification approach is introduced for closed‐loop systems under model‐based feedback control strategies. The linear models are re‐identified on‐line when significant prediction errors occur. A moving horizon error detector is used to quantify the model accuracy and to trigger the model re‐identification on‐line when necessary. The proposed approach is demonstrated through two chemical process examples using a model‐based feedback control strategy termed Lyapunov‐based economic model predictive control (LEMPC). The chemical process examples illustrate that the proposed error‐triggered on‐line model identification strategy can be used to obtain more accurate state predictions to improve process economics while maintaining closed‐loop stability of the process under LEMPC. © 2016 American Institute of Chemical Engineers AIChE J, 63: 949–966, 2017     

基于循环自动编码器的间歇过程故障监测

针对间歇过程数据非线性、动态性特征,提出一种基于循环自动编码器(recurrent autoencoder,RAE)的过程故障监测方法。采用长短时记忆(long short-term memory,LSTM)循环神经网络构建自动编码器建立监控模型,相比传统自动编码器,其能有效挖掘时序样本间的动态关联信息。该方法首先利用批次展开与变量展开相结合的三步展开方法将间歇过程数据展开成二维,并通过滑动窗采样得到模型输入序列;然后使用LSTM构建自动编码器,重构输入序列。进一步,利用重构误差构造平方预测误差(squared prediction error, SPE)统计量实现在线监测。最后将所提方法应用于青霉素发酵仿真和重组大肠杆菌发酵过程监测,结果表明,该方法能及时监测到故障,具有较好的监测性能。