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学者姓名:乔俊飞
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Abstract :
For a nonlinear parabolic distributed parameter system (DPS), a fuzzy boundary sampled-data (SD) control method is introduced in this article, where distributed SD measurement and boundary SD measurement are respected. Initially, this nonlinear parabolic DPS is represented precisely by a Takagi-Sugeno (T-S) fuzzy parabolic partial differential equation (PDE) model. Subsequently, under distributed SD measurement and boundary SD measurement, a fuzzy boundary SD control design is obtained via linear matrix inequalities (LMIs) on the basis of the T-S fuzzy parabolic PDE model to guarantee exponential stability for closed-loop parabolic DPS by using inequality techniques and a LF. Furthermore, respecting the property of membership functions, we present some LMI-based fuzzy boundary SD control design conditions. Finally, the effectiveness of the designed fuzzy boundary SD controller is demonstrated via two simulation examples.
Keyword :
boundary SD measurement boundary SD measurement parabolic distributed parameter system (DPS) parabolic distributed parameter system (DPS) fuzzy control fuzzy control distributed SD measurement distributed SD measurement Boundary sampled-data (SD) control Boundary sampled-data (SD) control
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GB/T 7714 | Wang, Zi-Peng , Li, Qian-Qian , Qiao, Junfei et al. Fuzzy Boundary Sampled-Data Control for Nonlinear Parabolic DPSs [J]. | IEEE TRANSACTIONS ON CYBERNETICS , 2023 . |
MLA | Wang, Zi-Peng et al. "Fuzzy Boundary Sampled-Data Control for Nonlinear Parabolic DPSs" . | IEEE TRANSACTIONS ON CYBERNETICS (2023) . |
APA | Wang, Zi-Peng , Li, Qian-Qian , Qiao, Junfei , Wu, Huai-Ning , Huang, Tingwen . Fuzzy Boundary Sampled-Data Control for Nonlinear Parabolic DPSs . | IEEE TRANSACTIONS ON CYBERNETICS , 2023 . |
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Abstract :
城市固废焚烧(MSWI)过程产生的二英(DXN)是至今机理仍复杂不清的剧毒污染物,获悉DXN在炉排炉内的生成、燃烧和再生成等过程的边界条件对降低污染排放极为重要。对此,本文提出了城市固废炉排炉焚烧过程DXN排放浓度数值仿真方法。首先,依据面向DXN的典型炉排炉MSWI工艺流程,描述焚烧炉内固相燃烧、气相燃烧、高温换热和低温换热等与DXN相关反应的机理。接着,依据上述所划分区域,结合实际MSWI过程相关参数构建DXN数值仿真模型。最后,基于烟气分流分率所表征的反应物浓度和不同区域的反应温度进行单因素分析,以获取G1处DXN浓度的边界条件,并基于正交实验分析分流分率和反应温度对G1处DXN浓度的影响,进而获得最优参数组合。基于北京某MSWI电厂实际数据的数值仿真分析与验证,表明了该数值仿真模型的有效性,为后续优化控制G1处的DXN排放浓度提供了支撑。
Keyword :
城市固废焚烧 城市固废焚烧 正交实验 正交实验 最优参数 最优参数 二英 二英 单因素分析 单因素分析 数值仿真 数值仿真
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GB/T 7714 | 陈佳昆 , 汤健 , 夏恒 et al. 城市固废炉排炉焚烧过程二英排放浓度数值仿真 [J]. | 化工进展 , 2023 , 42 (02) : 1061-1072 . |
MLA | 陈佳昆 et al. "城市固废炉排炉焚烧过程二英排放浓度数值仿真" . | 化工进展 42 . 02 (2023) : 1061-1072 . |
APA | 陈佳昆 , 汤健 , 夏恒 , 乔俊飞 . 城市固废炉排炉焚烧过程二英排放浓度数值仿真 . | 化工进展 , 2023 , 42 (02) , 1061-1072 . |
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Abstract :
Aiming at the nonstationary characteristics of many practical industrial systems as well as the long memory and seasonality of some process data, this article proposes a novel nonstationary process anomaly detection method based on fractional cointegration vector autoregression (FCVAR). First, the augmented Dickey-Fuller (ADF) test is used to divide the variables into stationary and nonstationary categories. For the nonstationary variables, the trend extraction algorithm is used to extract the nonstationary trend of the variables to avoid the trend information from being overwhelmed due to the strong seasonality of the process data. Meanwhile, considering that the extracted trend time series have a long-memory characteristic and the fractional cointegration describes the intrinsic long-term equilibrium relationship of the trend series better than the integer cointegration, a novel anomaly detection algorithm based on the FCVAR model is proposed. For the stationary variables, including the original stationary variables and the detrended series of nonstationary variables after trend extraction, the proposed method merges the two components into a new matrix and establishes an anomaly detection model based on the kernel principal component analysis (kpca) algorithm. Finally, simulations using wastewater treatment process (WWTP) data have indicated that the proposed method achieves the desired results and exhibits high detection performance, particularly in the detection of tiny gradual-type anomalies.
Keyword :
nonstationary system nonstationary system fractional cointegration fractional cointegration kernel principal component analysis (KPCA) kernel principal component analysis (KPCA) long-memory system long-memory system Anomaly detection Anomaly detection fractional cointegration vector autoregression (FCVAR) fractional cointegration vector autoregression (FCVAR)
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GB/T 7714 | Zhang, Ruiyao , Zhou, Ping , Qiao, Junfei . Anomaly Detection of Nonstationary Long-Memory Processes Based on Fractional Cointegration Vector Autoregression [J]. | IEEE TRANSACTIONS ON RELIABILITY , 2023 , 72 (4) : 1383-1394 . |
MLA | Zhang, Ruiyao et al. "Anomaly Detection of Nonstationary Long-Memory Processes Based on Fractional Cointegration Vector Autoregression" . | IEEE TRANSACTIONS ON RELIABILITY 72 . 4 (2023) : 1383-1394 . |
APA | Zhang, Ruiyao , Zhou, Ping , Qiao, Junfei . Anomaly Detection of Nonstationary Long-Memory Processes Based on Fractional Cointegration Vector Autoregression . | IEEE TRANSACTIONS ON RELIABILITY , 2023 , 72 (4) , 1383-1394 . |
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Abstract :
For nonlinear space-varying parabolic distributed parameter systems, this paper introduces an H-infinity fuzzy intermittent boundary control, where the output measurements are only available at some specified boundary position (i.e., boundary measurements). Initially, a Takagi-Sugeno fuzzy parabolic partial differential equation is used to precisely describe the nonlinear space-varying parabolic distributed parameter system. Then, under boundary measurements, an H-infinity fuzzy intermittent boundary control design based on the Takagi-Sugeno fuzzy parabolic partial differential equation model ensuring the exponential stability with an H-infinity performance for closed-loop space-varying distributed parameter system is subsequently obtained via spatial linear matrix inequalities by employing inequality techniques and piecewise switching-time-dependent Lyapunov function. Furthermore, in order to solve the H-infinity fuzzy intermittent boundary controller design of nonlinear space-varying parabolic distributed parameter systems under boundary measurements, we express spatial linear matrix inequalities as linear matrix inequalities and further present some linear matrix inequality based fuzzy intermittent boundary control design conditions respecting the property of membership functions. Finally, two simulation examples are offered to demonstrate the effectiveness of the proposed H-infinity fuzzy intermittent boundary control method. (c) 2023 The Franklin Institute. Published by Elsevier Inc. All rights reserved.
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GB/T 7714 | Wang, Zi-Peng , Zhao, Feng-Liang , Wu, Huai-Ning et al. H-infinity fuzzy intermittent boundary control for nonlinear parabolic distributed parameter systems [J]. | JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS , 2023 , 360 (12) : 8008-8036 . |
MLA | Wang, Zi-Peng et al. "H-infinity fuzzy intermittent boundary control for nonlinear parabolic distributed parameter systems" . | JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 360 . 12 (2023) : 8008-8036 . |
APA | Wang, Zi-Peng , Zhao, Feng-Liang , Wu, Huai-Ning , Qiao, Junfei , Huang, Tingwen . H-infinity fuzzy intermittent boundary control for nonlinear parabolic distributed parameter systems . | JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS , 2023 , 360 (12) , 8008-8036 . |
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Abstract :
Time series is mostly with a chaotic nature and non-stationary characteristic in real-word, which makes it difficult to be modeled and predicted accurately. To solve this problem, we introduce a novel self-organizing modular neural network based on the empirical mode decomposition with the sliding window mechanism (SWEMD-MNN) for time series prediction. In SWEMD-MNN, the improved empirical mode decomposition with sliding window (SWEMD) is developed to decompose time series online, which can effectively alleviate the limitation that the traditional EMD-based models cannot handle the long term or online problem and end effect. Thus, SWEMD-MNN can decompose time series based on time characteristic effectively and dynamically, and improve the prediction accuracy of the classical modular neural networks dividing time series based on sample space. Then time subseries are dynamically assigned to the subnetworks with a single layer feedforward neural network using the sample entropy and Euclidean distance for learning. Experimental investigations using benchmark chaotic and real-world time series show that SWEMD-MNN can decompose time series effectively and dynamically, and provides a better prediction accuracy than the fully coupled networks and other MNN models for time series prediction.& COPY; 2023 Elsevier B.V. All rights reserved.
Keyword :
Empirical mode decomposition Empirical mode decomposition Modular neural network Modular neural network Time series prediction Time series prediction Sample entropy Sample entropy Euclidean distance Euclidean distance
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GB/T 7714 | Guo, Xin , Li, Wen-jing , Qiao, Jun-fei . A self-organizing modular neural network based on empirical mode decomposition with sliding window for time series prediction [J]. | APPLIED SOFT COMPUTING , 2023 , 145 . |
MLA | Guo, Xin et al. "A self-organizing modular neural network based on empirical mode decomposition with sliding window for time series prediction" . | APPLIED SOFT COMPUTING 145 (2023) . |
APA | Guo, Xin , Li, Wen-jing , Qiao, Jun-fei . A self-organizing modular neural network based on empirical mode decomposition with sliding window for time series prediction . | APPLIED SOFT COMPUTING , 2023 , 145 . |
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Abstract :
针对构建城市固废焚烧(municipal solid waste incineration, MSWI)过程剧毒污染物二噁英(dioxin, DXN)排放风险预警模型的样本极为稀少的问题,提出一种基于主动学习机制生成对抗网络(generative adversarial network, GAN)的DXN排放风险预警建模方法.首先,以DXN风险等级作为条件信息使得GAN生成候选虚拟样本;然后,利用基于最大均值差异和多视角可视化分布信息的主动学习机制进行虚拟样本的初筛和评估,以获得期望虚拟样本;最后,基于混合样本构建DXN排放风险预警模型.通过基准数据集和MSWI过程数据集验证了所提方法的有效性.基于主动学习机制GAN的DXN排放风险预警建模方法可以有效解决样本稀少的问题,提高模型精度.
Keyword :
虚拟样本生成(virtual sample generation 虚拟样本生成(virtual sample generation 城市固废焚烧(municipal solid waste incineration 城市固废焚烧(municipal solid waste incineration 二噁英(dioxin 二噁英(dioxin VSG) VSG) MSWI) MSWI) 生成对抗网络(generative adversarial network 生成对抗网络(generative adversarial network 最大均值差异 最大均值差异 主动学习 主动学习 DXN)排放风险预警 DXN)排放风险预警 GAN) GAN)
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GB/T 7714 | 汤健 , 崔璨麟 , 夏恒 et al. 基于主动学习机制GAN的MSWI过程二噁英排放风险预警模型 [J]. | 北京工业大学学报 , 2023 , 49 (05) : 507-522 . |
MLA | 汤健 et al. "基于主动学习机制GAN的MSWI过程二噁英排放风险预警模型" . | 北京工业大学学报 49 . 05 (2023) : 507-522 . |
APA | 汤健 , 崔璨麟 , 夏恒 , 王丹丹 , 乔俊飞 . 基于主动学习机制GAN的MSWI过程二噁英排放风险预警模型 . | 北京工业大学学报 , 2023 , 49 (05) , 507-522 . |
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Abstract :
针对城市固废焚烧(municipal solid waste incineration,MSWI)过程中存在的随机和连续数据缺失问题,提出了一种基于专家经验和约简特征集成模型的填充方法.首先,将过程数据缺失情况识别为随机分布、时间维度和特征维度缺失3种类型.接着,基于专家经验对前2种类型进行缺失填充后,面向第3种类型基于分布相似性和互信息相关性为缺失特征预测模型选择建模数据集和约简特征,建立具有互补特性的随机森林、梯度提升决策树和反向传播神经网络子模型对缺失值进行初步预测,利用贝叶斯线性回归(Bayesian linear regression,BLR)构建集成模型以获得最终填充值.最后,利用填充后的MSWI数据建立基于跨层全连接深度森林回归的二噁英排放浓度软测量模型.实验结果表明所提方法提高了MSWI过程数据的质量.
Keyword :
集成模型 集成模型 BLR) BLR) 约简特征 约简特征 城市固废焚烧(municipal solid waste incineration 城市固废焚烧(municipal solid waste incineration 贝叶斯线性回归(Bayesian linear regression 贝叶斯线性回归(Bayesian linear regression 专家经验 专家经验 MSWI) MSWI) 数据填充 数据填充
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GB/T 7714 | 汤健 , 徐雯 , 夏恒 et al. 面向城市固废焚烧过程的缺失数据填充及应用 [J]. | 北京工业大学学报 , 2023 , 49 (4) : 435-448 . |
MLA | 汤健 et al. "面向城市固废焚烧过程的缺失数据填充及应用" . | 北京工业大学学报 49 . 4 (2023) : 435-448 . |
APA | 汤健 , 徐雯 , 夏恒 , 乔俊飞 . 面向城市固废焚烧过程的缺失数据填充及应用 . | 北京工业大学学报 , 2023 , 49 (4) , 435-448 . |
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城市固废焚烧(Municipal solid waste incineration,MSWI)是处置城市固废(Municipal solid waste,MSW)的主要手段之一.中国MSW来源范围广、组分复杂、热值波动大,其焚烧过程通常依靠人工干预,这导致MSWI过程智能化水平较低且难以满足日益提升的控制需求.MSWI具有多变量耦合、工况漂移等诸多不确定性特征,因而难以建立其被控对象模型并设计在线控制器.针对以上问题,提出了一种面向MSWI过程的数据驱动建模与自组织控制方法.首先,构建了基于多输入多输出Takagi Sugeno模糊神经网络(Multi-input multi-output Takagi Sugeno fuzzy neural network,MIMO-TSFNN)的被控对象模型;然后,设计了基于多任务学习的自组织模糊神经网络控制器(Multi-task learning self-organizing fuzzy neural network controller,MTL-SOFNNC)用于同步控制炉膛温度与烟气含氧量,其通过计算神经元的相似度与多任务学习(Multi-task learning,MTL)能力对控制器结构进行自组织调整;接着,通过Lyapunov定理对MTL-SOFNNC稳定性进行了证明;最后,通过北京市某MSWI厂的过程数据验证了模型与控制器的有效性.
Keyword :
多任务学习 多任务学习 模糊神经网络 模糊神经网络 自组织控制 自组织控制 数据驱动建模 数据驱动建模 城市固废焚烧 城市固废焚烧
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GB/T 7714 | 丁海旭 , 汤健 , 乔俊飞 . 城市固废焚烧过程数据驱动建模与自组织控制 [J]. | 自动化学报 , 2023 , 49 (3) : 550-566 . |
MLA | 丁海旭 et al. "城市固废焚烧过程数据驱动建模与自组织控制" . | 自动化学报 49 . 3 (2023) : 550-566 . |
APA | 丁海旭 , 汤健 , 乔俊飞 . 城市固废焚烧过程数据驱动建模与自组织控制 . | 自动化学报 , 2023 , 49 (3) , 550-566 . |
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Abstract :
鉴于城市固废焚烧(municipal solid waste incineration,MSWI)过程控制系统的封闭特性与工业现场的安全性要求,如何实现离线多模态数据的时间同步发布和如何搭建数据驱动预测模型的类工业现场验证环境,是实现智能建模算法落地应用需首要解决的关键问题。该文开发多模态数据驱动的MSWI过程验证平台,由多模态历史数据同步子系统和多模态历史数据驱动建模子系统组成。首先,结合现场领域专家预测关键工艺参数过程的抽象化描述,设计验证平台的结构;然后,建立以炉膛温度、烟气含氧量和锅炉蒸汽流量为输出的多模态数据驱动预测模型;最后,搭建硬件环境并开发相应的软件系统,实现子系统间的协同运行。利用实际过程数据与火焰视频验证该平台能够解决多模态数据驱动预测模型构建中存在的采样难、同步难、匹配难等问题,能够提供可靠的工程化验证环境。
Keyword :
验证平台 验证平台 多模态数据同步 多模态数据同步 关键工艺参数 关键工艺参数 预测模型 预测模型 城市固废焚烧 城市固废焚烧
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GB/T 7714 | 王天峥 , 汤健 , 夏恒 et al. 多模态数据驱动的城市固废焚烧过程验证平台设计与实现 [J]. | 中国电机工程学报 , 2023 , 43 (12) : 4697-4708 . |
MLA | 王天峥 et al. "多模态数据驱动的城市固废焚烧过程验证平台设计与实现" . | 中国电机工程学报 43 . 12 (2023) : 4697-4708 . |
APA | 王天峥 , 汤健 , 夏恒 , 潘晓彤 , 乔俊飞 , 刘溪芷 . 多模态数据驱动的城市固废焚烧过程验证平台设计与实现 . | 中国电机工程学报 , 2023 , 43 (12) , 4697-4708 . |
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针对城市污水处理过程时滞导致难以稳定控制的问题,提出一种自适应滑模控制方法 (Adaptive sliding mode control, ASMC).首先,分析推流时滞对城市污水处理生化反应过程的影响,建立时滞影响下的城市污水处理运行控制模型;其次,设计一种基于模糊神经网络的预估补偿模型,完成滞后变量的准确预测,实现控制模型中变量时刻的统一;最后,设计一种具有自适应开关增益系数的滑模控制器(Sliding mode control, SMC),实现溶解氧和硝态氮的稳定控制.将提出的自适应滑模控制方法应用于城市污水处理过程基准仿真平台,实验结果显示该方法能够实现城市污水处理运行过程稳定控制.
Keyword :
滑模控制 滑模控制 时滞 时滞 模糊神经网络 模糊神经网络 城市污水处理过程 城市污水处理过程
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GB/T 7714 | 韩红桂 , 秦晨辉 , 孙浩源 et al. 城市污水处理过程自适应滑模控制 [J]. | 自动化学报 , 2023 , 49 (05) : 1010-1018 . |
MLA | 韩红桂 et al. "城市污水处理过程自适应滑模控制" . | 自动化学报 49 . 05 (2023) : 1010-1018 . |
APA | 韩红桂 , 秦晨辉 , 孙浩源 , 乔俊飞 . 城市污水处理过程自适应滑模控制 . | 自动化学报 , 2023 , 49 (05) , 1010-1018 . |
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