检索结果-内蒙古大学图书馆

Online Fault-Tolerant Tracking Control With Adaptive Critic for Nonaffine Nonlinear systems

IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 215-227页

作者： Ding Wang Lingzhi Hu Xiaoli Li Junfei Qiao IEEE the School of Information Science and Technology Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Laboratory of Smart Environmental Protection and Beijing Institute of Artificial Intelligence Beijing University of Technology

In this paper, a fault-tolerant-based online critic learning algorithm is developed to solve the optimal tracking control issue for nonaffine nonlinear systems with actuator ***, a novel augmented plant is constructed by fusing the system state and the reference trajectory, which aims to transform the optimal fault-tolerant tracking control design with actuator faults into the optimal regulation problem of the conventional nonlinear error system. Subsequently, in order to ensure the normal execution of the online learning algorithm, a stability criterion condition is created to obtain an initial admissible tracking policy. Then, the constructed model neural network(NN) is pretrained to recognize the system dynamics and calculate trajectory control. The critic and action NNs are constructed to output the approximate cost function and approximate tracking control,respectively. The Hamilton-Jacobi-Bellman equation of the error system is solved online through the action-critic framework. In theoretical analysis, it is proved that all concerned signals are uniformly ultimately bounded according to the Lyapunov *** tracking control law can approach the optimal tracking control within a finite approximation error. Finally, two experimental examples are conducted to indicate the effectiveness and superiority of the developed fault-tolerant tracking control scheme.

关键词： Adaptive critic control neural network (NN) nonaffine nonlinear systems online fault-tolerant tracking design uniform ultimate boundedness

来源：评论

学校读者我要写书评

暂无评论

Learning Class-Aware Local Representations for Few-Shot Remote Sensing Scene Classification

引用

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 13225-13237页

作者： Wang, Liu Zhuo, Li Zhang, Hui Li, Jiafeng Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124 China

Few-Shot Remote Sensing Scene Classification (FSRSSC) aims at recognizing novel categories with only a few labeled samples. Local representations can retain richer details than image-level feature vectors and adapt well to the characteristics of spatially scattered objects in remote sensing images. However, the existing local representation-based few-shot learning works generally overlook the role of class-semantic information. To address this issue, in this article, a Class-aware Local Representation Learning (CLRL) method is proposed for FSRSSC. It generates class-aware support local representations by calibrating the local features with class-level global features to alleviate the problem of ignoring the class-semantic information. The Image-to-Class (I2C) metric is used to measure the similarities between query samples and support samples. Moreover, a novel Local Representation-based Self-supervised Learning (LRSL) strategy is proposed. The main idea of LRSL is to maximize the relationship consistency between the query local representations and different views of support local representations. A novel consistency loss is further proposed as the objective function of LRSL to supervise the model training. Different from the existing image-level feature vector-based self-supervised learning tasks, the proposed LRSL forces the model to capture the structure information of local representations. A robust model can be trained by jointly optimizing LRSL tasks and FSRSSC tasks. The experimental results on the NWPU-RESISC45, WHU-RS19, and UC Merced datasets show the superiority of our proposed method. © 2025 The Authors.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Fractal autoencoder with redundancy regularization for unsupervised feature selection

引用

Science China(Information Sciences) 2025年第2期68卷 89-102页

作者： Meiting SUN Fangyu LI Honggui HAN School of Information Science and Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Digital Community Ministry of Education Beijing Institute of Artificial Intelligence

Feature selection is a crucial step in data preprocessing because feature selection reduces the dimensionality of data by eliminating irrelevant and redundant features. Since manual labeling is expensive, unsupervised feature selection has received increasing attention in recent years. However, existing unsupervised feature selection methods tend to prioritize selecting highly correlated features over exploring feature diversity. Thus, a regularized fractal autoencoder(RFAE) method is proposed to select informative features in an unsupervised way. Specifically, the fractal autoencoder network extends autoencoders to construct a correspondence neural network and a selection neural network. The correspondence neural network exploits interfeature correlations and the selection neural network selects the informative features. A redundancy regularization strategy consists of a redundancy elimination regularization term based on the dependency between features and a sparse regularization term based on the group lasso. The redundancy regularization strategy eliminates feature subset redundancy and enhances network generalization ability. Extensive experimental results on six publicly available datasets show that the proposed RFAE outperforms the compared methods regarding clustering accuracy and classification accuracy. Moreover, the proposed RFAE achieves acceptable computation efficiency.

关键词： unsupervised feature selection fractal autoencoder correspondence neural network selection neural network redundancy regularization strategy

来源：评论

学校读者我要写书评

暂无评论

A hybrid attention model based on first-order statistical features for smoke recognition

引用

Science China(Technological Sciences) 2024年第3期67卷 809-822页

作者： GUO Nan LIU JiaHui DI KeXin GU Ke QIAO JunFei Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Artificial Intelligence InstituteBeijing 100124China Faculty of Information Technology Beijing University of TechnologyBeijing 100124China

Smoke and fire recognition are of great importance on foreseeing fire disasters and preventing environmental pollution by monitoring the burning process of objects(e.g., straw, fuels). However, since fire images suffer from problems like the variability of the features, complexity of scenarios, interference from background, changeable weather conditions as well as image quality problems, identifying smoke and fire accurately and promptly from a given image still remains a substantial challenge. Automatically learning the features of smoke images by CNNs has improved the target recognition ability compared to traditional approaches,nonetheless, convolutions and pooling operations in CNNs may cause severe information loss which may lead to *** tackle the above problems, this paper proposed a hybrid attention model based on the characteristics of smoke images. This model adopted multiple optimized attention mechanism in several stages to quickly and precisely capture the important features,achieving state-of-the-art performance on smoke and fire recognition in terms of accuracy and speed. Our proposed module mainly consists of two stages: pooling and attention. In the first stage, we conducted several newly proposed first-order pooling methods. Through traversing the data space in a larger scope, features are better reserved, thus constructing a more intact feature space of smoke and fire in an image. In the second stage, feature maps are aggregated together to perform channel and spatial attention. The channel and spatial dependencies allow us to quickly catch the important features presented in an image. By fully exploring the feature space and prominent salient features, characteristics of smoke and fire are better presented so as to obtain better smoke and fire detection results. Experiments have been conducted on public smoke detection dataset and new proposed fine-grained smoke and fire detection database. Experimental results revealed that the propose

关键词： hybrid attention first-order pooling smoke and fire detection deep convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Prototype Enhancement-Based Incremental Evolution Learning for Urban Garbage Classification

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial intelligence 2024年第1期5卷 398-411页

作者： Han, Honggui Fan, Xiaoye Li, Fangyu Beijing University of Technology Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Institute of Artificial Intelligence Beijing100124 China

Deep neural network (DNN) based on incremental learning provides support for efficient garbage classification tasks. However, it is always challenging to accurately learn and preserve the information of known classes for updating DNN while new tasks are continuously emerging, which also affects the generalization performance of the model. To solve these issues, an incremental evolution learning (IEL) method based on prototype enhancement is proposed to accurately preserve data and improve the model generalization ability. First, a prototype enhancement method based on multidimensional Gaussian kernel density estimation is designed, which extends the prototype of each sample based on high-dimensional nonlinear data distribution. Then, the prototype enhancement accurately represents the known class data. Second, a contrastive feature method is proposed to constrain the consistency of features between tasks, which reduces the deviation between different tasks. Then, the extraction preference caused by the class sample imbalance is balanced and the generalization ability is improved. Third, the proposed IEL is used for garbage classification with imbalanced class samples. IEL implements garbage classification by effectively adapting to the differences between known classes and new classes. Finally, experiments on four standard datasets and one public garbage dataset verify that IEL has a strong classification ability in the learning tasks with the increasing number of classes. © 2020 IEEE.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Hierarchical Learning-Based Integrated Robust Optimal Control for Nonlinear systems

引用

IEEE Transactions on systems, Man, and Cybernetics: systems 2025年第5期55卷 3119-3129页

作者： Zhang, Jiacheng Wang, Jingjing Han, Honggui Hou, Ying Huang, Yanting Beijing University of Technology Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124 China

The optimal control of nonlinear systems is crucial to improve system performance. However, the uncertainties of cost functions and systems dynamics make it difficult to solve the optimal control laws. To cope with this problem, a hierarchical learning-based integrated robust optimal control (HL-IROC) method is proposed in this article. The merits of the proposed HL-IROC method are three aspects: First, a hierarchical learning-based integrated optimal control (HL-IOC) scheme, contains a system dynamic learning layer and a cost function learning layer, is designed to transform the original optimal control problem into an integrated optimization problem with control laws as decision variables. Then, the relationships between cost functions and control laws are captured to overcome the difficulties brought by uncertainties in the optimal control process. Second, a global-local cooperative robust evolutionary optimization (GL-CREO) algorithm is proposed to obtain the optimal control laws. Then, a global-local robust searching strategy is employed to deal with two types of uncertainties for improving the robustness of control laws. Third, the convergence analysis of HL-IOC and GL-CREO is discussed in theory. In the experiments, the effectiveness of HL-IROC is illustrated with a nonlinear system and a wastewater treatment process. © 2013 IEEE.

关键词： Optimal control systems

来源：评论

学校读者我要写书评

暂无评论

A second-order knowledge filter transfer learning algorithm for modeling nonlinear systems

引用

Science China Technological Sciences 2025年第6期68卷 105-120页

作者： Honggui HAN Mengmeng LI Xiaolong WU Hongyan YANG Junfei QIAO School of Information Science and Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Digital Community Ministry of Education

Transfer learning algorithms can transform prior knowledge into linearization knowledge to model nonlinear systems. However, the linearization knowledge-based models tend to diverge in the process of knowledge linearization due to the neglected information of higher-order terms. To overcome this problem, a second-order knowledge filter transfer learning algorithm(SOFTLA) is developed for modeling nonlinear systems. First, a knowledge transformation strategy is introduced to transform the linearization source knowledge into comprehensive knowledge containing first-order and second-order *** with the original knowledge, the transformed source knowledge with second-order term can prevent information loss during the knowledge linearization. Second, a knowledge filter algorithm is proposed to eliminate the useless information in the source knowledge. Subsequently, a suitable filter gain is designed to reduce the cumulative error in knowledge updating process. Third, a model adaptation mechanism is designed to enable effective knowledge transfer by updating the structure and parameters of the target model simultaneously. Subsequently, the adaptability of the source knowledge is enhanced to facilitate learning tasks in the target domain. Finally, a benchmark problem and several practical industrial applications are presented to validate the superiority of SOFTLA. The experimental discussions illustrate that SOFTLA can obtain obvious advantages over contrastive methods.

关键词： transfer learning knowledge transformation strategy knowledge filter algorithm model adaptation mechanism

来源：评论

学校读者我要写书评

暂无评论

Adaptive Multi-Step Evaluation Design With Stability Guarantee for Discrete-Time Optimal Learning Control

引用

IEEE/CAA Journal of Automatica Sinica 2023年第9期10卷 1797-1809页

作者： Ding Wang Jiangyu Wang Mingming Zhao Peng Xin Junfei Qiao IEEE Faculty of Information Technology the Beijing Key Laboratory of Computational Intelligence and Intelligent Systemthe Beijing Laboratory of Smart Environmental Protectionand the Beijing Institute of Artificial IntelligenceBeijing University of TechnologyBeijing 100124China

This paper is concerned with a novel integrated multi-step heuristic dynamic programming(MsHDP)algorithm for solving optimal control *** is shown that,initialized by the zero cost function,MsHDP can converge to the optimal solution of the Hamilton-Jacobi-Bellman(HJB)***,the stability of the system is analyzed using control policies generated by ***,a general stability criterion is designed to determine the admissibility of the current control *** is,the criterion is applicable not only to traditional value iteration and policy iteration but also to ***,based on the convergence and the stability criterion,the integrated MsHDP algorithm using immature control policies is developed to accelerate learning efficiency ***,actor-critic is utilized to implement the integrated MsHDP scheme,where neural networks are used to evaluate and improve the iterative policy as the parameter ***,two simulation examples are given to demonstrate that the learning effectiveness of the integrated MsHDP scheme surpasses those of other fixed or integrated methods.

关键词： Adaptive critic artificial neural networks Hamilton-Jacobi-Bellman(HJB)equation multi-step heuristic dynamic programming multi-step reinforcement learning optimal control

来源：评论

学校读者我要写书评

暂无评论

Fuzzy Neural Network-Based Adaptive Asymmetric Constraint Control in Wastewater Treatment Process

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial intelligence 2024年第6期5卷 3284-3296页

作者： Chen, Dingyuan Yang, Cuili Qiao, Junfei Beijing University of Technology Faculty of Information Technology Beijing Laboratory for Intelligent Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Institute of Artificial Intelligence Beijing100124 China

Wastewater treatment process (WWTP) is an important mean to prevent water pollution and improve ecological environment. Dissolved oxygen (DO) and nitrate nitrogen (NO3-N) concentrations are the main indicators to affect the effluent quality (EQ). In order to achieve high accuracy control, the artificial intelligence based asymmetric constraint control method with actuator saturation processing technology is proposed in WWTP. First, the fuzzy neural network (FNN) model is used to estimate the unknown situations, in which the maximum correlation entropy criterion is introduced into the adjustment of model structure to deal with the dynamic changes of WWTP. Second, the unified actuator saturation processing model is established to achieve the stable tracking performance. Third, the asymmetric barrier Lyapunov function (BLF) is introduced into the controller design. Not only the DO and NO3-N concentrations, but also the tracking error are kept within the asymmetric constraint range to guarantee the control performance. Finally, the effectiveness of the proposed method is verified via the dynamical and constant set-points simulation experiments of benchmark simulation model 1 (BSM1). © 2020 IEEE.

关键词： Wastewater treatment

来源：评论

学校读者我要写书评

暂无评论

Online Aware Synapse Weighted Autoencoder for Recovering Random Missing Data in Wastewater Treatment Process

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial intelligence 2024年第2期5卷 578-589页

作者： Han, Honggui Sun, Meiting Li, Fangyu Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Digital Community Ministry of Education Beijing University of Technology Beijing100124 China Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing100124 China

Missing values in wastewater treatment process (WWTP) data hinder the monitoring and prediction of operational status. Therefore, various online imputation methods have been proposed to recover missing values from streaming data collected from WWTP from real time. However, existing methods tend to ignore previous learned knowledge. In this article, an online aware synapse weighted autoencoder imputation method (OASI) is proposed to impute random missing values. First, an online stacked autoencoder (OSAE) framework is constructed to capture the nonlinear structure of the recently collected data. The framework decreases the computational and storage consumption of the model training. Second, an aware synapses weighted parameter regularization strategy is presented to guide the update of model parameters and alleviate the forgetting of historical information in an online continual setup. In this way, the learned features offer a more comprehensive representation of the overall information and help enhance imputation performance. Third, two real WWTP datasets with strong nonstationarity, high-noise level and high-dimensionality are used to validate the performance of the proposed OASI. Experimental results show that the proposed OASI achieves superior performances over the existing methods even in the presence of random missing values with different missing ratios, and only costs a short running time. © 2023 IEEE.

关键词： Real time systems

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：