7 results on '"Hu, Jingtao"'
Search Results
2. Deterministic learning-based neural identification and knowledge fusion.
- Author
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Wu, Weiming, Hu, Jingtao, Zhu, Zejian, Zhang, Fukai, Xu, Juanjuan, and Wang, Cong
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DISCRETE-time systems , *SYSTEM identification , *GROUP work in education , *SYSTEM dynamics , *COMPUTATIONAL complexity , *DETERMINISTIC algorithms , *REINFORCEMENT learning - Abstract
Recent deterministic learning methods have achieved locally-accurate identification of unknown system dynamics. However, the locally-accurate identification means that the neural networks can only capture the local dynamics knowledge along the system trajectory. In order to capture a broader knowledge region, this article investigates the knowledge fusion problem of deterministic learning, that is, the integration of different knowledge regions along different individual trajectories. Specifically, two kinds of knowledge fusion schemes are systematically introduced: an online fusion scheme and an offline fusion scheme. The online scheme can be viewed as an extension of distributed cooperative learning control to cooperative neural identification for sampled-data systems. By designing an auxiliary information transmission strategy to enable the neural network to receive information learned from other tasks while learning its own task, it is proven that the weights of all localized RBF networks exponentially converge to their common true/ideal values. The offline scheme can be regarded as a knowledge distillation strategy, in which the fused network is obtained by offline training through the knowledge learned from all individual system trajectories via deterministic learning. A novel weight fusion algorithm with low computational complexity is proposed based on the least squares solution under subspace constraints. Simulation studies show that the proposed fusion schemes can successfully integrate the knowledge regions of different individual trajectories while maintaining the learning performance, thereby greatly expanding the knowledge region learned from deterministic learning. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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3. Observer-based dynamical pattern recognition via deterministic learning.
- Author
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Hu, Jingtao, Wu, Weiming, Zhang, Fukai, Chen, Tianrui, and Wang, Cong
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RECOGNITION (Psychology) , *RADIAL basis functions , *TIME series analysis , *DYNAMICAL systems , *COMPUTATIONAL complexity , *DETERMINISTIC algorithms , *PATTERN recognition systems - Abstract
In this paper, based on the sampled-data observer and the deterministic learning theory, a rapid dynamical pattern recognition approach is proposed for univariate time series composed of the output signals of the dynamical systems. Specifically, locally-accurate identification of inherent dynamics of univariate time series is first achieved by using the sampled-data observer and the radial basis function (RBF) networks. The dynamical estimators embedded with the learned knowledge are then designed by resorting to the sampled-data observer. It is proved that generated estimator residuals can reflect the difference between the system dynamics of the training and test univariate time series. Finally, a recognition decision-making scheme is proposed based on the residual norms of the dynamical estimators. Through rigorous analysis, recognition conditions are given to guarantee the accurate recognition of the dynamical pattern of the test univariate time series. The significance of this paper lies in that the difficult problems of dynamical modeling and rapid recognition for univariate time series are solved by incorporating the sampled-data observer design and the deterministic learning theory. The effectiveness of the proposed approach is confirmed by a numerical example and compressor stall warning experiments. • An observer-based recognition approach is proposed for univariate time series. • The challenge of learning dynamical systems from univariate time series is overcome. • Estimators perform recognition tasks, reducing computational complexity. • Average residual norms are used for decisions, which ensures recognition accuracy. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
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4. Observer Design for Sampled-Data Systems via Deterministic Learning.
- Author
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Hu, Jingtao, Wu, Weiming, Ji, Bing, and Wang, Cong
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DISCRETE-time systems , *RADIAL basis functions , *NONLINEAR systems , *DETERMINISTIC processes , *ITERATIVE learning control , *ARTIFICIAL neural networks , *ADAPTIVE fuzzy control - Abstract
A unified approach is proposed to design sampled-data observers for a certain type of unknown nonlinear systems undergoing recurrent motions based on deterministic learning in this article. First, a discrete-time implementation of high-gain observer (HGO) is utilized to obtain state trajectory from sampled output measurements. By taking the recurrent estimated trajectory as inputs to a dynamical radial basis function network (RBFN), a partial persistent exciting (PE) condition is satisfied, and a locally accurate approximation of nonlinear dynamics can be realized along the estimated sampled-data trajectory. Second, an RBFN-based observer consisting of the obtained dynamics from the process of deterministic learning is designed. Without resorting to high gains, the RBFN-based observer is shown capable of achieving correct state observation. The novelty of this article lies in that, by incorporating deterministic learning with the discrete-time HGO, the nonlinear dynamics can be accurately approximated along the estimated trajectory, and such obtained knowledge can then be utilized to realize nonhigh-gain state estimation for the same or similar sampled-data systems. Simulation is performed to validate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
5. Deterministic learning from neural control for a class of sampled-data nonlinear systems.
- Author
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Zhang, Fukai, Wu, Weiming, Hu, Jingtao, and Wang, Cong
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DISCRETE-time systems , *NONLINEAR systems , *ADAPTIVE control systems , *EXPONENTIAL stability , *CLOSED loop systems - Abstract
This study investigates the deterministic learning and control issues for uncertain sampled-data nonlinear systems (SDNSs). The problem of how to acquire/learn knowledge from adaptive control for SDNSs with uncertain affine terms is studied. To be specific, an appropriate neural network-based (NNB) control strategy is first presented to ensure tracking performance. To further realize learning, the exponential stability (ES) of the integrated closed-loop system coupled with the estimation error of the NN weights is considered. As the uncertain affine term prevents learning from occurring, the integrated system is converted into a discrete linear time-varying (DLTV) perturbed system by employing the state conversion technique. Tracking convergence allows the persistent excitation condition (PEC) of the NNs to be established, which guarantees the ES of the integrated DLTV system. Thus, accurate modeling of closed-loop sampled dynamics is obtained. By reutilizing the experiential knowledge obtained, a knowledge-based controller is constructed for high-performance control. Finally, simulations are performed to verify the presented strategy. [ABSTRACT FROM AUTHOR]
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- 2022
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6. Explicit convergence relations for a class of discrete LTV systems and its application to performance analysis of deterministic learning.
- Author
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Wu, Weiming, Zhang, Jinyuan, Hu, Jingtao, and Wang, Cong
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DISCRETE systems , *ITERATIVE learning control , *ADAPTIVE control systems , *EXPONENTIAL stability , *PARAMETER identification , *LYAPUNOV functions - Abstract
A class of linear time-varying (LTV) systems commonly appears in classical adaptive control and identification, in which the accurate identification of parameters is highly related to their exponential stability. However, there is limited research on explicit convergence relations for discrete LTV systems. In this article, the explicit convergence relation of a class of discrete LTV systems is first established, in which strict Lyapunov functions are constructed by considering the convergence properties of the interconnected unforced subsystems. Next, based on the derived explicit convergence relation, a performance analysis of deterministic learning under the sampling-data framework is established. We show that the learning speed and learning accuracy increase with the persistent excitation (PE) level and decrease with the identifier gain. Moreover, an optimal learning gain exists related to the identifier gains. To illustrate the results, simulation studies are included. [ABSTRACT FROM AUTHOR]
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- 2024
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7. Integrating reinforcement learning with deterministic learning for fault diagnosis of nonlinear systems.
- Author
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Zhu, Zejian, Wu, Weiming, Chen, Tianrui, Hu, Jingtao, and Wang, Cong
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NONLINEAR systems , *FAULT diagnosis , *INDUSTRIAL safety , *ENGINEERING systems , *DISCRETE-time systems , *NONLINEAR dynamical systems - Abstract
Reliable fault diagnosis (FD) is important to ensure safety in nonlinear engineering systems. Modern engineering systems are often subject to unknown complex nonlinearities and varying operation conditions, therefore, one of the main challenges for FD of nonlinear systems is the robustness against these uncertainties. In this paper, a novel robust FD approach combining the reinforcement learning (RL) and the deterministic learning theory (DLT) is developed for a class of discrete-time nonlinear systems. The DLT is employed to pre-train the neural network (NN) aiming at approximating the unknown nonlinear complexity and obtaining dynamical fault models, then RL techniques are employed to adapt the NN parameters to improve the robustness of fault models. The stability of the learning process is rigorously analyzed using Lyapunov-based methods, and the effectiveness of the presented method is validated by a rotating stall warning experiment based on the data from Beihang University compressor test rig. Experiment results demonstrate that compared with other methods, the proposed method can achieve better performance in lead warning time and robustness. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
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