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学者姓名：乔俊飞
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Abstract ：
Broad learning system based on neural network (BLS-NN) has poor efficiency for small data modeling with various dimensions. Tree-based BLS (TBLS) is designed for small data modeling by introducing nondifferentiable modules and an ensemble strategy to the traditional broad learning system (BLS). TBLS replaces the neurons of BLS with the tree modules to map the input data. Moreover, we present three new TBLS variant methods and their incremental learning implementations, which are motivated by deep, broad, and ensemble learning. Their major distinction is reflected in the incremental learning strategies based on: 1) mean square error (mse); 2) pseudo-inverse; and 3) pseudo-inverse theory and stack representation. Therefore, this study further explores the domain of BLS based on the nondifferentiable modules. The simulations are compared with some state-of-the-art (SOTA) BLS-NN and tree methods under high-, medium-, and low-dimensional benchmark datasets. Results show that the proposed method outperforms the BLS-NN, and the modeling accuracy is remarkably improved with the small training data of the proposed TBLS.
Keyword ：
BLS-based on neural network (BLS-NN) BLS-based on neural network (BLS-NN) small data modeling small data modeling broad learning system (BLS) broad learning system (BLS) tree BLS (TBLS) tree BLS (TBLS)
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| GB/T 7714 | Xia, Heng , Tang, Jian , Yu, Wen et al. Tree Broad Learning System for Small Data Modeling [J]. | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS , 2024 , 35 (7) : 8909-8923 . |
| MLA | Xia, Heng et al. "Tree Broad Learning System for Small Data Modeling" . | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 35 . 7 (2024) : 8909-8923 . |
| APA | Xia, Heng , Tang, Jian , Yu, Wen , Qiao, Junfei . Tree Broad Learning System for Small Data Modeling . | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS , 2024 , 35 (7) , 8909-8923 . |
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Abstract ：
The real-time detection technique and comprehensive characterization of dioxin (DXN) emission concentration during the municipal solid waste incineration process persist as unresolved challenges. Prevailing research predominantly relies on data-driven models, often overlooking the potential benefits derived from fusing combustion mechanism knowledge. To confront this issue, we propose a hybrid modeling strategy that fuses a simulator-based mechanism model with an enhanced regression decision tree-based data model. This approach aims to predict DXN emission concentrations while accommodating diverse time-scaled measurement requirements. Based on virtual mechanism data obtained via numerical simulation models coupling FLIC and Aspen Plus, we constructed a white-box surrogate model utilizing a multiple-input multiple-output linear regression decision tree (LRDT). To establish a relationship with DXN emission concentration, we employed a semisupervised transfer learning mapping model. It was then fused with a novel ensemble LRDT model based on real historical data by using a constrained incremental random weight neural network. The efficacy of this modeling strategy was validated through an industrial application case study conducted in Beijing.
Keyword ：
Numerical models Numerical models municipal solid waste incineration (MSWI) municipal solid waste incineration (MSWI) MIMO communication MIMO communication Data models Data models Mathematical models Mathematical models multidemand modeling multidemand modeling Predictive models Predictive models Solid modeling Solid modeling Combustion Combustion linear regression decision tree (LRDT) linear regression decision tree (LRDT) mechanism-driven (MD) and data-driven (DD) mechanism-driven (MD) and data-driven (DD) numerical simulation numerical simulation Dioxin (DXN) Dioxin (DXN) semisupervised transfer learning semisupervised transfer learning
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| GB/T 7714 | Xia, Heng , Tang, Jian , Yu, Wen et al. Hybrid Simulator-Based Mechanism and Data-Driven for Multidemand Dioxin Emissions Intelligent Prediction in the MSWI Process [J]. | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS , 2024 . |
| MLA | Xia, Heng et al. "Hybrid Simulator-Based Mechanism and Data-Driven for Multidemand Dioxin Emissions Intelligent Prediction in the MSWI Process" . | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2024) . |
| APA | Xia, Heng , Tang, Jian , Yu, Wen , Qiao, Junfei . Hybrid Simulator-Based Mechanism and Data-Driven for Multidemand Dioxin Emissions Intelligent Prediction in the MSWI Process . | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS , 2024 . |
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Abstract ：
For nonlinear distributed parameter systems (DPSs) with multiple delays, this study considers a fuzzy fault-tolerant boundary control (BC) with stochastic actuator failures under boundary measurement. First, we exactly represent the nonlinear DPS with multiple delays by the Takagi-Sugeno (T-S) fuzzy delayed partial differential equation (PDE). Next, on basis of T-S fuzzy delayed PDE model, a fuzzy fault-tolerant BC design with stochastic actuator failures under boundary measurement guaranteeing the stochastically exponential stability for closed-loop DPS with multiple delays is subsequently presented by linear matrix inequalities. Last, the effectiveness of the investigated fuzzy fault-tolerant BC strategy with stochastic actuator failures under boundary measurement is proposed via a simulation example.
Keyword ：
Actuators Actuators Linear matrix inequalities Linear matrix inequalities Delays Delays Fault tolerant systems Fault tolerant systems Fault tolerance Fault tolerance Boundary measurement Boundary measurement distributed parameter system (DPS) distributed parameter system (DPS) stochastic actuator failures stochastic actuator failures Biological system modeling Biological system modeling multiple delays multiple delays Stochastic processes Stochastic processes fuzzy fault-tolerant boundary control (BC) fuzzy fault-tolerant boundary control (BC)
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| GB/T 7714 | Wang, Zi-Peng , Zhang, Xu , Qiao, Junfei et al. Fuzzy Fault-Tolerant Boundary Control for Nonlinear DPSs With Multiple Delays and Stochastic Actuator Failures [J]. | IEEE TRANSACTIONS ON FUZZY SYSTEMS , 2024 , 32 (5) : 3121-3131 . |
| MLA | Wang, Zi-Peng et al. "Fuzzy Fault-Tolerant Boundary Control for Nonlinear DPSs With Multiple Delays and Stochastic Actuator Failures" . | IEEE TRANSACTIONS ON FUZZY SYSTEMS 32 . 5 (2024) : 3121-3131 . |
| APA | Wang, Zi-Peng , Zhang, Xu , Qiao, Junfei , Wu, Huai-Ning , Huang, Tingwen . Fuzzy Fault-Tolerant Boundary Control for Nonlinear DPSs With Multiple Delays and Stochastic Actuator Failures . | IEEE TRANSACTIONS ON FUZZY SYSTEMS , 2024 , 32 (5) , 3121-3131 . |
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Abstract ：
This article addresses fault-tolerant event-triggered sampled-data (SD) fuzzy control for nonlinear delayed parabolic partial differential equation (PDE) systems under spatially point measurements (SPMs). First, a Takagi-Sugeno fuzzy delayed PDE model is presented to accurately describe the nonlinear delayed parabolic PDE systems. Second, a fault-tolerant event-triggered SD fuzzy control strategy under SPMs is designed to cope with Markov jump faults occurring in actuators, which can effectively reduce the unnecessary data transmission and be achieved by finite sensors and actuators. The membership functions of the proposed controller are determined by the measurement output and independent of the fuzzy delayed PDE plant model. Then, by constructing a Lyapunov functional, sufficient conditions that guarantee the stochastically exponential stability of closed-loop nonlinear delayed parabolic PDE systems are obtained based on linear matrix inequalities. Finally, two examples are given to illustrate the designed approach.
Keyword ：
spatially point measurements (SPMs) spatially point measurements (SPMs) Markov jump actuator failures Markov jump actuator failures Fault-tolerant event-triggered sampled-data (SD) fuzzy control Fault-tolerant event-triggered sampled-data (SD) fuzzy control nonlinear delayed parabolic partial differential equation (PDE) system nonlinear delayed parabolic partial differential equation (PDE) system
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| GB/T 7714 | Chen, Bo-Ming , Wang, Zi-Peng , Zhao, Feng-Liang et al. Fault-Tolerant Event-Triggered Sampled-Data Fuzzy Control for Nonlinear Delayed Parabolic PDE Systems [J]. | IEEE TRANSACTIONS ON FUZZY SYSTEMS , 2024 , 32 (11) : 6460-6471 . |
| MLA | Chen, Bo-Ming et al. "Fault-Tolerant Event-Triggered Sampled-Data Fuzzy Control for Nonlinear Delayed Parabolic PDE Systems" . | IEEE TRANSACTIONS ON FUZZY SYSTEMS 32 . 11 (2024) : 6460-6471 . |
| APA | Chen, Bo-Ming , Wang, Zi-Peng , Zhao, Feng-Liang , Qiao, Junfei , Wu, Huai-Ning , Huang, Tingwen . Fault-Tolerant Event-Triggered Sampled-Data Fuzzy Control for Nonlinear Delayed Parabolic PDE Systems . | IEEE TRANSACTIONS ON FUZZY SYSTEMS , 2024 , 32 (11) , 6460-6471 . |
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Abstract ：
A mixed fuzzy intermittent control method based on boundary control under boundary measurement and distributed control under spatial local averaged measurements (SLAMs) is introduced for nonlinear ordinary differential equations (ODE)-partial differential equations(PDE) coupled systems in this article. To accurately characterize the nonlinear ODE-PDE coupled systems, a Takagi-Sugeno fuzzy model is first employed. Then, based on the fuzzy model, the switched Lyapunov function is proposed to design the mixed fuzzy intermittent controller under boundary measurement and SLAMs. Sufficient conditions on stability for the closed-loop coupled system are obtained via a set of space dependent linear matrix inequalities. The simulation results ultimately confirm the effectiveness of the proposed design approach in controlling hypersonic rocket car.
Keyword ：
space- dependent linear matrix inequalities (SDLMIs) space- dependent linear matrix inequalities (SDLMIs) Hypersonic rocket car (HRC) Hypersonic rocket car (HRC) mixed fuzzy intermittent control mixed fuzzy intermittent control nonlinear ordinary differential equations- partial differential equations (ODE-PDE) coupled systems nonlinear ordinary differential equations- partial differential equations (ODE-PDE) coupled systems
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| GB/T 7714 | Wang, Zi-Peng , Su, Hua-Ran , Shi, Xi-Dong et al. Mixed Fuzzy Intermittent Control for Nonlinear ODE-PDE Coupled Systems [J]. | IEEE TRANSACTIONS ON FUZZY SYSTEMS , 2024 , 32 (12) : 6658-6670 . |
| MLA | Wang, Zi-Peng et al. "Mixed Fuzzy Intermittent Control for Nonlinear ODE-PDE Coupled Systems" . | IEEE TRANSACTIONS ON FUZZY SYSTEMS 32 . 12 (2024) : 6658-6670 . |
| APA | Wang, Zi-Peng , Su, Hua-Ran , Shi, Xi-Dong , Qiao, Junfei , Wu, Huai-Ning , Huang, Tingwen et al. Mixed Fuzzy Intermittent Control for Nonlinear ODE-PDE Coupled Systems . | IEEE TRANSACTIONS ON FUZZY SYSTEMS , 2024 , 32 (12) , 6658-6670 . |
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Abstract ：
Transfer learning can handle the domain adaptation of different feature spaces in nonlinear systems. Most existing studies only focus on common features between heterogeneous scenes rather than specific features that account for latent similarities between them. To deal with this problem, a filter transfer learning algorithm for nonlinear systems modeling with heterogeneous features is proposed. First, nonlinear mapping is constructed to learn the potential relationships between different feature attributes, including common features and specific features. Then, source knowledge from different domains can be obtained in the form of process parameters according to the mapping relationship. Second, a hierarchical filter framework is presented to reconstruct source knowledge in different transfer phases. In the pre-transfer phase, a knowledge filter is designed to increase the diversity of knowledge through selection, crossover, and mutation operations. In the post-transfer phase, a guided filter is established to achieve a coupling balance between source knowledge and target domain by using target samples as guidance information. Third, a dynamic parameter learning strategy is given to promote the learning performance of heterogeneous tasks. Finally, the effectiveness of innovations and the superiority of this proposed algorithm are verified by the experimental results in nonlinear systems with heterogeneous features.
Keyword ：
Nonlinear mapping Nonlinear mapping Transfer learning Transfer learning Heterogeneous features Heterogeneous features Hierarchical filter framework Hierarchical filter framework
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| GB/T 7714 | Han, Honggui , Li, Mengmeng , Wu, Xiaolong et al. Filter transfer learning algorithm for nonlinear systems modeling with heterogeneous features [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2024 , 260 . |
| MLA | Han, Honggui et al. "Filter transfer learning algorithm for nonlinear systems modeling with heterogeneous features" . | EXPERT SYSTEMS WITH APPLICATIONS 260 (2024) . |
| APA | Han, Honggui , Li, Mengmeng , Wu, Xiaolong , Yang, Hongyan , Qiao, Junfei . Filter transfer learning algorithm for nonlinear systems modeling with heterogeneous features . | EXPERT SYSTEMS WITH APPLICATIONS , 2024 , 260 . |
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Abstract ：
Owing to high complexity and time-variant operation, as well as increasingly requirements for water quality, stability, and reliability, the wastewater treatment process (WWTP) is regarded as an adaptive control problem. In this study, a data-driven adaptive control with deep learning (DRAC-DL) is developed to improve the operational performance of the WWTP. First, a feedback controller is designed to construct the closed-loop control scheme. Second, an adaptive deep belief network (ADBN), based on the data-driven self-incremental learning strategy, is proposed to approximate the ideal control law. Third, the stability of the DRAC-DL scheme is analyzed in detail. The main advantage of DRAC-DL lies in its improved robustness and efficiency, which benefit from the Lyapunov-based closed-loop strategy and the efficient ADBN controller. Finally, the feasibility and applicability of DRAC-DL are verified by two parts: 1) simulation on the nonlinear system and 2) application to the WWTP on the benchmark simulation model No.1. The experimental results show the applicability and effectiveness, among which DRAC-DL reduces the output fluctuation (variance) by no less than 82% and realizes the better stability and robustness.
Keyword ：
stability analysis stability analysis wastewater treatment process (WWTP) wastewater treatment process (WWTP) Adaptive control Adaptive control adaptive deep belief network (ADBN) adaptive deep belief network (ADBN)
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| GB/T 7714 | Wang, Gongming , Zhao, Yidi , Liu, Caixia et al. Data-Driven Robust Adaptive Control With Deep Learning for Wastewater Treatment Process [J]. | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2024 , 20 (1) : 149-157 . |
| MLA | Wang, Gongming et al. "Data-Driven Robust Adaptive Control With Deep Learning for Wastewater Treatment Process" . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 20 . 1 (2024) : 149-157 . |
| APA | Wang, Gongming , Zhao, Yidi , Liu, Caixia , Qiao, Junfei . Data-Driven Robust Adaptive Control With Deep Learning for Wastewater Treatment Process . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2024 , 20 (1) , 149-157 . |
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Abstract ：
This paper introduces a fuzzy intermittent control issue for nonlinear PDE-ODE coupled system under spatially point measurements (SPMs), which can be represented by an ordinary differential equation (ODE) and a partial differential equation (PDE). Firstly, the nonlinear coupled system is aptly characterized by the Takagi-Sugeno (T-S) fuzzy PDE-ODE coupled model. Subsequently, based on T-S fuzzy model, a novel Lyapunov function (LF) is provided to design a fuzzy intermittent controller ensuring exponential stability of the closed-loop coupled system. The stabilization conditions are presented by means of a group of space-dependent linear matrix inequalities (SDLMIs). Finally, simulation results are given to illustrate the effectiveness of the proposed design method in the control of a hypersonic rocket car (HRC).
Keyword ：
Fuzzy intermittent controller Fuzzy intermittent controller Nonlinear PDE-ODE coupled systems Nonlinear PDE-ODE coupled systems Spatially point measurements (SPMs) Spatially point measurements (SPMs) Hypersonic rocket car (HRC) Hypersonic rocket car (HRC)
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| GB/T 7714 | Shi, Xi-Dong , Wang, Zi-Peng , Qiao, Junfei et al. Fuzzy Intermittent Control for Nonlinear PDE-ODE Coupled Systems [J]. | INTERNATIONAL JOURNAL OF FUZZY SYSTEMS , 2024 , 26 (8) : 2585-2601 . |
| MLA | Shi, Xi-Dong et al. "Fuzzy Intermittent Control for Nonlinear PDE-ODE Coupled Systems" . | INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 26 . 8 (2024) : 2585-2601 . |
| APA | Shi, Xi-Dong , Wang, Zi-Peng , Qiao, Junfei , Wu, Huai-Ning , Zhang, Xiao-Wei , Yan, Xue-Hua . Fuzzy Intermittent Control for Nonlinear PDE-ODE Coupled Systems . | INTERNATIONAL JOURNAL OF FUZZY SYSTEMS , 2024 , 26 (8) , 2585-2601 . |
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Abstract ：
Under intermittent noncollocated boundary measurement (BM), this article introduces a dynamic intermittent boundary output-feedback control for reaction-diffusion systems (RDSs). Since the system state is not fully available and intermittent noncollocated BM makes the intermittent BC design very difficult, an observer-based control technique is given to surmount this design difficulty. Initially, a PDE state observer under intermittent noncollocated BM is provided to estimate the RDS state. Then, the exponential stability of closed-loop RDSs is ensured by constructing an observer-based controller. Sufficient conditions of such dynamic controller are subsequently presented by linear matrix inequalities (LMIs) via employing a switching time-dependent LF and inequality techniques. Finally, two numerical examples are presented to demonstrate the effectiveness of the proposed design approach.
Keyword ：
reaction-diffusion systems (RDSs) reaction-diffusion systems (RDSs) Dynamic intermittent boundary control (DIBC) Dynamic intermittent boundary control (DIBC) intermittent noncollocated boundary measurement (BM) intermittent noncollocated boundary measurement (BM)
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| GB/T 7714 | Wang, Zi-Peng , Zhao, Feng-Liang , Qiao, Junfei et al. Dynamic Intermittent Boundary Control for Reaction-Diffusion Systems Under Intermittent Noncollocated Boundary Measurement [J]. | IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS , 2024 . |
| MLA | Wang, Zi-Peng et al. "Dynamic Intermittent Boundary Control for Reaction-Diffusion Systems Under Intermittent Noncollocated Boundary Measurement" . | IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS (2024) . |
| APA | Wang, Zi-Peng , Zhao, Feng-Liang , Qiao, Junfei , Wu, Huai-Ning , Huang, Tingwen . Dynamic Intermittent Boundary Control for Reaction-Diffusion Systems Under Intermittent Noncollocated Boundary Measurement . | IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS , 2024 . |
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Abstract ：
In many fields, spatiotemporal prediction is gaining more and more attention, e.g., air pollution, weather forecasting, and traffic forecasting. Water quality prediction is a spatiotemporal prediction task. However, there are several challenges in water quality prediction: 1) Water quality time series has a complex nonlinear relationship, making it difficult to predict; 2) Water quality sensors are distributed on the river networks and have a strong spatial dependence on water quality prediction; and 3) Poor long-term forecast accuracy. To solve these problems, this work proposes a spatiotemporal prediction model called a Fusion Spatio-temporal Graph Convolution Neural network (FSGCN). First, This work uses a temporal attention mechanism to solve the nonlinear problem of water quality time series. Second, It adopts a graph convolution to extract spatial dependencies of river networks, and the fusion of spatiotemporal can more easily capture spatiotemporal features. Third, it adopts a temporal convolution residual mechanism, improving long-term series prediction accuracy. This work adopts two real-world datasets to evaluate the proposed FSGCN, and experiments demonstrate that FSGCN outperforms several state-of-the-art methods in terms of prediction accuracy.
Keyword ：
river network river network graph convolution neural network graph convolution neural network spatiotemporal fusion spatiotemporal fusion temporal convolution residual temporal convolution residual Water quality prediction Water quality prediction
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| GB/T 7714 | Qiao, Junfei , Lin, Yongze , Bi, Jing et al. Attention-Based Spatiotemporal Graph Fusion Convolution Networks for Water Quality Prediction [J]. | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING , 2024 . |
| MLA | Qiao, Junfei et al. "Attention-Based Spatiotemporal Graph Fusion Convolution Networks for Water Quality Prediction" . | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING (2024) . |
| APA | Qiao, Junfei , Lin, Yongze , Bi, Jing , Yuan, Haitao , Wang, Gongming , Zhou, Mengchu . Attention-Based Spatiotemporal Graph Fusion Convolution Networks for Water Quality Prediction . | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING , 2024 . |
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