Query：
学者姓名：乔俊飞
Refining：
Year
Type
Indexed by
Source
Complex
Former Name
Co-Author
Language
Clean All
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)
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
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)
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
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)
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
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)
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Abstract ：
Dioxin (DXN) is a persistent organic pollutant produced from municipal solid waste incineration (MSWI) processes. It is a crucial environmental indicator to minimize emission concentration by using optimization control, but it is difficult to monitor in real time. Aiming at online soft-sensing of DXN emission, a novel fuzzy tree broad learning system (FTBLS) is proposed, which includes offline training and online measurement. In the offline training part, weighted k-means is presented to construct a typical sample pool for reduced learning costs of offline and online phases. Moreover, the novel FTBLS, which contains a feature mapping layer, enhance layer, and increment layer, by replacing the fuzzy decision tree with neurons applied to construct the offline model. In the online measurement part, recursive principal component analysis is used to monitor the time-varying characteristic of the MSWI process. To measure DXN emission, offline FTBLS is reused for normal samples; for drift samples, fast incremental learning is used for online updates. A DXN data from the actual MSWI process is employed to prove the usefulness of FTBLS, where the RMSE of training and testing data are 0.0099 and 0.0216, respectively. This result shows that FTBLS can effectively realize DXN online prediction.
Keyword ：
Decision trees Decision trees municipal solid waste incineration (MSWI) municipal solid waste incineration (MSWI) online soft-sensing online soft-sensing time-varying time-varying Training Training Pollution measurement Pollution measurement Dioxin (DXN) Dioxin (DXN) Frequency modulation Frequency modulation Principal component analysis Principal component analysis fuzzy tree broad learning system (FTBLS) fuzzy tree broad learning system (FTBLS) Data models Data models Monitoring Monitoring
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Xia, Heng , Tang, Jian , Yu, Wen et al. Online Measurement of Dioxin Emission in Solid Waste Incineration Using Fuzzy Broad Learning [J]. | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2024 , 20 (1) : 358-368 . |
| MLA | Xia, Heng et al. "Online Measurement of Dioxin Emission in Solid Waste Incineration Using Fuzzy Broad Learning" . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 20 . 1 (2024) : 358-368 . |
| APA | Xia, Heng , Tang, Jian , Yu, Wen , Qiao, Junfei . Online Measurement of Dioxin Emission in Solid Waste Incineration Using Fuzzy Broad Learning . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2024 , 20 (1) , 358-368 . |
| Export to | NoteExpress RIS BibTex |
Abstract ：
Coastal water quality prediction is the indispensable work to prevent the red tide and marine pollution accidents, which also provides the effective assistance to study ocean carbon sink. Due to the multiple inducing factors and their spatiotemporal coupling effects, the water quality prediction not only needs to be supported by big data, but also needs an effective model for prediction and analysis. However, most of the existing models frequently use timeline data from the same section or local collection point and cannot realize inversion and traceability of inducing factors. In this article, we consider these tough problems and propose an effective neurodynamics-driven prediction model for state evolution of coastal water quality (NDPM-CWQ). First, an event-driven deep belief network (EDBN) is designed and trained using the spatiotemporal data. Second, through the sensitivity analysis of the input variables in EDBN model, we rank influence degrees of spatiotemporal variables on the water quality and give the inversion and traceability of inducing factors. Third, the convergence of training EDBN is analyzed from the perspective of the stationary distribution and decision stability of Markov chain. Finally, the practical data-based experimental results show that the proposed NDPM-CWQ not only achieves better prediction performance, but also can quantitatively analyze the inversion and traceability of inducing factors.
Keyword ：
Predictive models Predictive models Analytical models Analytical models Couplings Couplings Data models Data models Training Training Sea measurements Sea measurements neurodynamics analysis neurodynamics analysis event-driven learning event-driven learning Biological system modeling Biological system modeling Coastal water quality prediction Coastal water quality prediction inversion and traceability of inducing factors inversion and traceability of inducing factors deep belief network deep belief network
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Wang, Gongming , Chen, Hong , Jiang, Suling et al. Neurodynamics-Driven Prediction Model for State Evolution of Coastal Water Quality [J]. | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2024 , 73 . |
| MLA | Wang, Gongming et al. "Neurodynamics-Driven Prediction Model for State Evolution of Coastal Water Quality" . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 73 (2024) . |
| APA | Wang, Gongming , Chen, Hong , Jiang, Suling , Han, Honggui , Qiao, Junfei . Neurodynamics-Driven Prediction Model for State Evolution of Coastal Water Quality . | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT , 2024 , 73 . |
| Export to | NoteExpress RIS BibTex |
Abstract ：
This article introduces a fuzzy boundary sampled-data (SD) control approach for a nonlinear distributed parameter system (DPS) with random time-varying delay, which belongs to two intervals and is considered by a probabilistic way to take the influence of uncertain factors, and boundary and distributed SD measurements are respected. Initially, this nonlinear DPS is represented precisely by a Takagi-Sugeno (T-S) fuzzy delayed partial differential equation (PDE) model. Subsequently, a fuzzy boundary SD control design is achieved under boundary and distributed SD measurements, employing linear matrix inequalities based on the T-S fuzzy delayed PDE model. This design ensures mean square exponential stability for the closed-loop delayed DPS through the use of inequality techniques and a Lyapunov functional. The membership functions of the proposed fuzzy boundary SD control law are independent of the fuzzy delayed PDE plant model and determined by the measurement output. Finally, the effectiveness of the designed fuzzy boundary SD controller is demonstrated via two simulation examples.
Keyword ：
Laboratories Laboratories distributed parameter system (DPS) distributed parameter system (DPS) Biological system modeling Biological system modeling Delays Delays Control design Control design Delay effects Delay effects boundary SD fuzzy control boundary SD fuzzy control Process control Process control random time-varying delays random time-varying delays Boundary and distributed sampled-data (SD) measurements Boundary and distributed sampled-data (SD) measurements Fuzzy control Fuzzy control
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Wang, Zi-Peng , Chen, Bo-Ming , Qiao, Junfei et al. Fuzzy Boundary Sampled-Data Control for Nonlinear DPSs With Random Time-Varying Delays [J]. | IEEE TRANSACTIONS ON FUZZY SYSTEMS , 2024 , 32 (10) : 5872-5885 . |
| MLA | Wang, Zi-Peng et al. "Fuzzy Boundary Sampled-Data Control for Nonlinear DPSs With Random Time-Varying Delays" . | IEEE TRANSACTIONS ON FUZZY SYSTEMS 32 . 10 (2024) : 5872-5885 . |
| APA | Wang, Zi-Peng , Chen, Bo-Ming , Qiao, Junfei , Wu, Huai-Ning , Huang, Tingwen . Fuzzy Boundary Sampled-Data Control for Nonlinear DPSs With Random Time-Varying Delays . | IEEE TRANSACTIONS ON FUZZY SYSTEMS , 2024 , 32 (10) , 5872-5885 . |
| Export to | NoteExpress RIS BibTex |
Export
| Results： |
Selected to |
| Format： |
