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检索条件"任意字段=7th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2018"

共 474 条记录，以下是201-210 订阅

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On Finite-Iteration Convergence of Iterative learning control 11

On Finite-Iteration Convergence of Iterative Learning Contro...

引用

11th ieee data driven control and learning systems conference, ddcls 2022

作者： Liu, Zhiqing Chi, Ronghu Liu, Yang Qingdao University of Science and Technology Qingdao266061 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

In this paper, a new problem of finite-iteration convergence of iterative learning control is discussed for a linear time-varying system. Considering a PD-type learning law, the finite-iteration convergence is shown by introducing two-dimensional system theory under some assumptions. By solving a set of linear matrix inequalities, the two learning gains of the control law can be updated in real time to ensure that the tracking error converges to an arbitrarily specified bound within finite iterative operations. Simulation study illustrates the correctness of the theoretical results. © 2022 ieee.

关键词： Linear matrix inequalities

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data-driven Optimal control for Half-Vehicle Suspension System via Adaptive Dynamic Programming 11

Data-Driven Optimal Control for Half-Vehicle Suspension Syst...

引用

11th ieee data driven control and learning systems conference, ddcls 2022

作者： Li, Hongyang Wei, Qinglai School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Institute of Systems Engineering Macau University of Science and Technology Macau999078 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

In this paper, a data-driven optimal control method is provided for the half-vehicle suspension system via adaptive dynamic programming. the main contribution of this paper is that the data-driven adaptive dynamic programming method is applied to the optimal control problem of half-vehicle suspension system, which only requires the input-state data of the system. First, the structure of the half-vehicle suspension system is analyzed, and the optimal control problem is introduced. Next, the model-based adaptive dynamic programming method is provided. Based on the model-based method, a data-driven adaptive dynamic programming method is given. the properties of the provided methods are analyzed. Finally, simulation example is given to show the effectiveness of the data-driven adaptive dynamic programming method. © 2022 ieee.

关键词： Dynamic programming

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H∞ control for singular markovian jump delay systems with mode-dependent derivative-term coefficient 8

H∞ control for singular markovian jump delay systems with m...

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8th ieee data driven control and learning systems conference, ddcls 2019

作者： Tian, Yufeng Wang, Zhanshan College of Information Science and Engineering Northeastern University Shenyang110819 China

ISBN: (纸本)9781728114545

this paper focuses on the $H-{\infty}$ control problem of singular Markovian jump delay systems with mode-dependent derivative-term coefficient through an extended decomposition system. By computing a proper Lyapunov functional, a generally stochastic stability condition of singular Markovian jump systems is achieved. On this basis, a delay-dependent stabilization condition of considered system is derived in terms of tractable linear matrix inequalities (LMIs), and the $H-{\infty}$ controller gains are directly designed. Two numerical examples are introduced to illustrate the effectiveness of the proposed results. © 2019 ieee.

关键词： Linear matrix inequalities

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data-driven Reconstruction for Massive Buildings within Urban Scenarios: A Case Study 9

Data-driven Reconstruction for Massive Buildings within Urba...

引用

9th ieee data driven control and learning systems conference (ddcls)

作者： Chen, Ziye Xiang, Xiaojia Zhou, Han Hu, Tianjiang Natl Univ Def Technol Coll Intelligence Sci & Technol Changsha 410073 Peoples R China Sun Yat Sen Univ Guangzhou 510725 Peoples R China

ISBN: (纸本)9781728159225

the reconstruction and visualization of urban environments is widely used in immersive simulation, but the massive data of buildings bring huge burden to the modelling and rendering process. this paper proposes an improved fast approach to build and visualize the scene graph based on the level of details (LOD). Firstly, an automatic reconstruction scheme of urban buildings is designed by using the public vector data set. Secondly, a tree-type organization and management structure is constructed for urban scenarios inspired by the idea of recursive division. thirdly, a dynamic data scheduling strategy is developed with concerns of self-organizing the generated 3D massive model data. the developed approach is called Detail Segmentation of Scene Tree Method, namely DSSTM. Finally, experiments are conducted for performance comparison between the proposed DSSTM and a conventional method, while both are realized by using the open source library, OpenSceneGraph, http://***/. the experimental results of reconstructing Changsha City have verified that the proposed method is effective and efficient for practical applications of visualization on large-scale urban with massive buildings. the rendering speed is increased to 4 similar to 7 times of the conventional method, the dynamic loading time is shortened by 90%.

关键词： three-dimensional reconstruction Scene tree massive scenario data scheduling

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Spectrum Attention Mechanism for Time Series Classification 10

Spectrum Attention Mechanism for Time Series Classification

引用

10th ieee data driven control and learning systems conference, ddcls 2021

作者： Zhou, Shibo Pan, Yu Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou310027 China

ISBN: (纸本)9781665424233

Time series classification (TSC) has always been an important and challenging research task. With the wide application of deep learning, more and more researchers use deep learning models to solve TSC problems. Since time series always contains a lot of noise, which has a negative impact on network training, the original data are usually filtered before the training of the network. the existing schemes are to treat the filtering and training as two stages, and the design of the filter requires expert experience, which increases the design difficulty of the algorithm and is not universal. We note that the essence of filtering is to filter out the insignificant frequency components and highlight the important ones, which is similar to the attention mechanism. In this paper, we propose an Spectrum Attention Mechanism (SAM). the network can assign appropriate weights to each frequency component to achieve adaptive filtering. We use l1 regularization to further enhance the frequency screening capability of the SAM. We also propose a Segmented-SAM (SSAM) to avoid the loss of time domain information caused by using the spectrum of the whole sequence. In which, a tumbling window is introduced to segment the original data. then SAM is applied to each segment to generate new features. We propose a heuristic strategy to search for the appropriate number of segments. Experimental results show that SSAM can produce better feature representations to speed up the learning process, and improve the robustness and classification accuracy. © 2021 ieee.

关键词： Time series

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A weighted gene co-expression network analysis based on iterative learning 8

A weighted gene co-expression network analysis based on iter...

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8th ieee data driven control and learning systems conference, ddcls 2019

作者： Yu, Meijuan Li, Yan Gong, Fengjun School of Control Science and Engineering Shandong University Jinan250061 China

ISBN: (纸本)9781728114545

Gene co-expression networks are widely used in field of exploring gene function and describe connections not only between genes in the network, but also between gene and trait of biological significance. As a part of building gene co-expression network, the adjacency function plays an important role in describing the connections between genes, which converts the co-expressive measure of each gene pair to its connective weight. A suitable parameter of adjacency function is determined by a biologically motivated criterion known as the scale-free topology fitting index in the gene co-expression network. In generally, the parameter of adjacency function is selected by manually looking up parameter table that is generated with the R software package WGCNA (Weighted Gene Co-Expression Network Analysis - WGCNA). therefore, the revised algorithm of selecting parameter of adjacency function is proposed. Iterative learning algorithm is used to automatically find the required parameter of adjacency function and the Mouse module-trait relationships are constructed. © 2019 ieee.

关键词： Iterative methods

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An Enhanced Fuzzy Repetitive-control Design and Optimisation Method based on 2D Performance Index 10

An Enhanced Fuzzy Repetitive-Control Design and Optimisation...

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Zhang, Manli Wu, Min Tian, Shengnan She, Jinhua School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China School of Engineering Tokyo University of Technology Hachioji192-0982 Japan

ISBN: (纸本)9781665424233

this paper concerns the design and optimisation of two-dimensional (2D) repetitive control of nonlinear systems based on the Takagi-Sugeno (T-S) fuzzy model. First, a continuous-discrete 2D model of nonlinear repetitive-control systems based on T-S fuzzy model is constructed by utilizing the 2D characteristics of continuous control and discrete learning actions in the repetitive-control process. Next, a fuzzy Lyapunov-Krasovskii functional derives the linear-matrix-inequality-based stability condition with a low conservatism. Two positive and two nonzero parameters in the fuzzy Lyapunov-Krasovskii functional tune the control and learning actions. then the particle swarm optimisation algorithm based on a 2D performance index searches for the best parameter combination, resulting in the optimal 2D controller gains. A numerical example is given to demonstrate the effectiveness of the method. © 2021 ieee.

关键词： Linear matrix inequalities

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Adaptive PID control for time-varying fermentation processes 10

Adaptive PID control for time-varying fermentation processes

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Wang, Zhiwen Chen, Chongcheng Chen, Xiaoling Li, Dagang Zeng, Feihu Fujian University Applied Technical Engineering Center of Practical Chemical Materials Liming Vocational University Quanzhou362000 China School of Light Industry Liming Vocational University Quanzhou362000 China School of New Materials and Shoes Clothing Engineering Liming Vocational University Quanzhou362000 China

ISBN: (纸本)9781665424233

PID controller is a common control regulator for industrial processes and is widely used in chemical processes. However, conventional proportional-integral-derivative controller has been found insufficient for nonlinear and time-varying fermentation processes. In this work, an auto-tuning neuron PID (ANPID) method based on particle swarm optimization is proposed to handle the time-varying fermentation process. Specifically, the PSO method is first adopted to initialize the parameters of PID. then, an ANPID controller is utilized to adaptively manipulate the process. the design advantage of ANPID is that its control parameters can be updated using useful historical information. Consequently, the time-varying behavior can be captured and good control performance can be achieved. Simulation results illustrate that the proposed controller can obtain better performance than traditional PID. © 2021 ieee.

关键词： three term control systems

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On Driver's Workload in Dangerous Scenes Based on EEG data 9

On Driver's Workload in Dangerous Scenes Based on EEG Data

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9th ieee data driven control and learning systems conference (ddcls)

作者： Tani, Jiyuan Bi, Rui Guo, Weiwei Li, Li Wang, Yueqin North China Univ Technol Beijing Key Lab Urban Intelligent Traff Control T Beijing 100144 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

ISBN: (纸本)9781728159225

Scientific measurement of the risk degree of traffic scenes and accurate assessment of driver's workload are conducive to reducing driving risk and road traffic accidents. In this paper, EEG signal evaluation method based on "driver's" perspective is used to describe the risk of traffic scene objectively and quantitatively. the traffic scenes with dynamic traffic environment factors are taken as the research objects, including the pedestrian scene and the variable-speed vehicle scene. the drivers' EEG signals are used as the indicators to evaluate the risk degree of the traffic scene. Based on research objects and indicators, the internal relationship between drivers' EEG signals and traffic dangerous environment factors are explored, and the evaluation models of traffic scene risk degree based on drivers' EEG signals are established.

关键词： Traffic scene risk degree EEG signal Workload Logistic regression mode

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Manufacturing Big data Modeling Based on KNN-LR Algorithm and Its Application in Product Design Business Domain 10

Manufacturing Big Data Modeling Based on KNN-LR Algorithm an...

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Xiao, Yi Ren, Hongru Lu, Renquan Cheng, Shen Sch. of Automat. and Guangdong Prov. Key Laboratory of Intelligent Decision and Cooperative Control Guangdong University of Technology Guangzhou510006 China

ISBN: (纸本)9781665424233

In product life cycle, it is very important to use the manufacturing big data to build prediction model and apply it to predict whether the design task of the product can be completed within the specified time. Most of the existing prediction models in manufacturing industry are built by a single algorithm or its improved version, and neglect the limitation of using a single forecasting algorithm, which may lead to poor forecasting accuracy. this paper aims to integrate the K-nearest neighbor classification algorithm and the logistic regression algorithm linearly in parallel to obtain the combined model which is called K-nearest neighbor-logistic regression (KNN-LR) in this paper, and use the combined model to predict whether the design task of the product can be completed within the specified time. Experimental results show that compared with the model built by a single algorithm, the combined model has better performance on model evaluation indicators such as accuracy, precision, F1 value. recall and classification error rate. © 2021 ieee.

关键词： Big data

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