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检索条件"任意字段=International Conference on Complex Systems: Control and Modeling Problems"

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Tackling Data Heterogeneity: A New Unified Framework for Decentralized SGD with Sample-induced Topology 39

Tackling Data Heterogeneity: A New Unified Framework for Dec...

引用

39th international conference on Machine Learning (ICML)

作者： Huang, Yan Sun, Ying Zhu, Zehan Yan, Changzhi Xu, Jinming Zhejiang Univ Coll Control Sci & Engn Hangzhou Peoples R China Penn State Univ Sch Elect Engn & Comp Sci University Pk PA 16802 USA

We develop a general framework unifying several gradient-based stochastic optimization methods for empirical risk minimization problems both in centralized and distributed scenarios. The framework hinges on the introduction of an augmented graph consisting of nodes modeling the samples and edges modeling both the inter-device communication and intra-device stochastic gradient computation. By designing properly the topology of the augmented graph, we are able to recover as special cases the renowned Local-SGD and DSGD algorithms, and provide a unified perspective for variance-reduction (VR) and gradient-tracking (GT) methods such as SAGA, Local-SVRG and GT-SAGA. We also provide a unified convergence analysis for smooth and (strongly) convex objectives relying on a proper structured Lyapunov function, and the obtained rate can recover the best known results for many existing algorithms. The rate results further reveal that VR and GT methods can effectively eliminate data heterogeneity within and across devices, respectively, enabling the exact convergence of the algorithm to the optimal solution. Numerical experiments confirm the findings in this paper.

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Research on Industrial Robot Trajectory Tracking control System based on PID Feedforward Algorithm 2

Research on Industrial Robot Trajectory Tracking Control Sys...

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2nd IEEE international conference on Electronic Technology, Communication and Information, ICETCI 2022

作者： Liang, Jiaxuan School of Mechanical Engineering Nanjing University of Science and Technology Jiangsu Nanjing210094 China

ISBN: (数字)9781728181158

ISBN: (纸本)9781728181158

In the design process of industrial robots, multi-input and multi-output control algorithms are usually required for nonlinear systems. This kind of control system has time-varying characteristics, and its dynamic characteristics have nonlinear problems, so it is very difficult to design. However, the dynamic control process of the robot is very complex, and the trajectory tracking control system needs to be redesigned to ensure that the control system can effectively track the acceleration and speed characteristics of the robot. Therefore, this paper studies the tracking control algorithm for the motion trajectory of the industrial robot. The research process mainly includes the analysis of the motion trajectory of the industrial robot. On this basis, the simulation is used to simplify the model and design the control system, and the PID and the feedforward algorithm are used to compensate and adjust the control system to improve the accuracy of its control tracking. In addition, this paper optimizes the PID algorithm, uses a fuzzy algorithm, and reaches law to improve the accuracy and quality of the control algorithm. Optimizing the trajectory tracking algorithm's response time and tracking accuracy is achieved. © 2022 IEEE.

关键词： Industrial robots

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The Problem of Concept Learning and Goals of Reasoning in Large Language Models 18th

The Problem of Concept Learning and Goals of Reasoning in La...

引用

18th international conference on Hybrid Artificial Intelligence systems

作者： Chuganskaya, Anfisa A. Kovalev, Alexey K. Panov, Aleksandr Russian Acad Sci Fed Res Ctr Comp Sci & Control Moscow Russia AIRI Moscow Russia

ISBN: (纸本)9783031407246;9783031407253

Modern large language models (LLMs) show good performance in the zero-shot or few-shot learning. This ability ability is a significant result even on tasks for which the models have not been trained is in part due to the fact that by learning from textual internet-scale data, such models build a semblance of a model of the world. However, the question of whether the entities on which that the model operates are concepts in the psychological sense remains open. Relying on conceptual reasoning schemes allows to increase the safety of models in solving complex problems. To address this question, we propose to use standard psychodiagnostic techniques to assess the quality of conceptual thinking of models. We test this hypothesis, by conducting experiments on a dataset adapted for LLMs from the psychological techniques of Kettel and Rubinstein and comparing the effectiveness of each of them. In this paper, we have shown that it is possible to distinguish several types of model errors in incorrect answers to standard tasks on conceptual thinking and to evaluate the type according to the classifications of distortions of conceptual thinking adopted in cultural and historical approaches in psychology. This makes it possible to use the tool of psychodiagnostic techniques not only to evaluate the effectiveness of models, but also to develop training procedures based on such tasks.

关键词： Concept learning Large language models Psychology of thinking Purpose of the activity Sign-based world model

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An Entropy Feedback Based Evolutionary Algorithms and its Application

An Entropy Feedback Based Evolutionary Algorithms and its Ap...

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Data-driven Optimization of complex systems (DOCS), international conference on

作者： Xilong Feng Guosheng Hao Yi Zhu Shijin Ren School of Computer Science and Technology Jiangsu Normal University Xuzhou China Jiangsu Wisdom-driven Research Institute Xuzhou China Xuzhou China

ISBN: (数字)9798350377842

ISBN: (纸本)9798350377859

Evolutionary algorithms (EAs) simulate the process of biological evolution in nature to solve problems. EAs are a class of global optimization methods with high robustness, and have been widely applied in many areas. Feedback control (FC) regulates the behaviors of the system based on feedback. To understand the insight of EAs better and analyze the reported EAs, this paper proposes an entropy feedback based evolutionary algorithms (EFEAs). The entropy is adopted to measure the evolutionary state and acts as the feedback for self-adaptive adjustment. Based on the feedback of entropy, the control parameters, such as ranking selection probability, crossover probability, and mutation probability, are adaptive adjusted to tune the evolutionary state and improve the search performance of EAs. Finally, the state feedback framework is applied to analyze the mechanism and performance of the improved EAs. EFEAs provides a new perspective for understanding the mechanisms of EAs and a new approach to improve the performance of EAs. Experiments in the Travelling Salesman Problem (TSP) show that EFEAs outperforms other comparison algorithms.

关键词： State feedback Evolution (biology) Optimization methods Evolutionary computation Entropy Robustness Feedback control complex systems

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modeling the Dynamics of the Territorial Structure of the Northern Part of the Volga-Akhtuba Floodplain 3

Modeling the Dynamics of the Territorial Structure of the No...

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3rd international conference on control systems, Mathematical modeling, Automation and Energy Efficiency, SUMMA 2021

作者： Voronin, Alexander A. Isaeva, Inessa I. Khoperskov, Alexander V. Kharitonov, Mikhail A. Volgograd State University Dept. of Fundamental Informatics and Optimal Control Volgograd Russia

ISBN: (纸本)9781665439817

A new approach to the analysis of the dynamics of the territorial structure of floodplain territories of regulated rivers, based on statistical processing of natural observation data, hydrodynamic and geoinformation modeling using high-performance computing, is described. The main tool of our analysis is a new model of the complex structure of the floodplain territory, which is a superposition of its hydrological, natural (ecological) and socio-economic structures. Hydrological simulation results are based on self-consistent models of surface water, sediment and groundwater dynamics using realistic digital terrain. © 2021 IEEE.

关键词： Banks (bodies of water)

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Blast Furnace Gas Utilization Rate Prediction Model based on Northern Goshawk Optimization and Long Short-Term Memory in Massive Data Set 6

Blast Furnace Gas Utilization Rate Prediction Model based on...

引用

IEEE 6th international conference on Industrial Cyber-Physical systems (ICPS)

作者： Zhou, Yue Cao, Weihua Zhang, Zhuofu Yuan, Yan China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Peoples R China

ISBN: (纸本)9798350311259

Affected by the global New Crown Pneumonia epidemic, energy prices in the global market continue to rise. The high quality production and energy saving of steel companies have become an important task. The blast furnace is the front-end core of the steel manufacturing process. The blast furnace gas utilization rate can effectively characterize the internal airflow distribution, blast furnace operation and energy consumption level of the blast *** blast furnace Gas Utilization Rate (GUR) can effectively characterize the internal airflow distribution, blast furnace operation and energy consumption level. Most of the existing studies are based on data-driven models. The shallow neural network model is selected. But blast furnace iron making has complex uncertainty. Massive data samples under industrial information technology need to be processed. The robustness and generalization ability of the prediction model are not satisfactory. To address the above problems, this paper proposes a prediction model based on NGO-LSTM regression. The model parameter searchs can be intelligently. It achieves high-precision prediction results for massive data samples. Firstly, the selection of feature parameters is completed by maximal information coefficient. Secondly, the strong coupling of blast furnace ground needs to fully predict the relationship between the characteristic parameters. The Long Short-Term Memory (LSTM) neural network with certain memory capability is selected. And multiple parameters of this neural network model are optimized by the Northern Goshawk Optimization (NGO) algorithm. An NGO-LSTM regression prediction model is established. In this paper, experiments are carried out using actual production data. The experimental results show that the proposed method can accurately predict the blast furnace gas utilization rate. This can provide a reference for improving blast furnace product quality, reducing costs and increasing efficiency.

关键词： blast furnance gas utilization rate prediction Long Short-Term Memory neural network Northern Goshawk Optimization

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Reliability Assessment of LCC-HVDC links Using Protection Devices

Reliability Assessment of LCC-HVDC links Using Protection De...

引用

Renewable Solutions for Ecosystems: Towards a Sustainable Energy Transition (ICRSEtoSET), international conference on

作者： Abdelhadi Hameurlaine Larbi Boukezzi Salah Karef Haouari Sayah Laboratory of Applied Automation & Industrial Diagnostics University of Djelfa Djelfa Algeria Materials Science and Informatics Laboratory MSIL University of Djelfa Djelfa Algeria Com Laboratory of Modeling Optimization and Simulation of Real Complex Systems University of Djelfa Djelfa Algeria Intelligent Control and Electrical Power System Laboratory University of SidiBel-Abbes SidiBel-Abbes Algeria

ISBN: (数字)9798350346336

ISBN: (纸本)9798350346343

the DC/AC interaction and the presence of the high nonlinearity in the HVDC links, these problems needs to examine the behavior of the system in steady-state and transient conditions in order to define the great role of protection functions and control parameters. Furthermore, control and protection systems are a vital part of an HVDC system to guarantee the safety, reliability, and stability of its operation. This research aim is to provide the solutions for control and protection problems that can occur in HVDC link. In this paper, we study how we mitigate this serious malfunction of commutation failure process in the operation of HVDC converters. Indeed some protection functions like the commutation failure prevention. Furthermore, HVDC transmission systems are vulnerable to DC faults and its protection becomes ever more important with the fast growth in the number of installations. Finally, the dynamics of the studied power system including the HVDC link is thoroughly investigated through various simulation scenarios.

关键词： Performance evaluation HVDC transmission Power system dynamics Power system stability Stability analysis Power system reliability Steady-state

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Improving Capabilities of Constitutive modeling of Shape Memory Alloys for Solving Dynamic problems Via Application of Neural Networks 1

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15th international conference Dynamical systems: Theory and Applications, DSTA, 2019

作者： Martowicz, Adam Żabiński, Mikolaj Bryla, Jakub Roemer, Jakub Department of Robotics and Mechatronics AGH University of Science and Technology Krakow Poland

ISBN: (数字)9783030773069

ISBN: (纸本)9783030773052

The paper addresses an issue of improving capabilities of the constitutive models elaborated for shape memory alloys (SMA) to solve dynamic problems. Artificial neural networks (ANN) are utilized to simulate the experimentally identified complex behavior of the mentioned type of smart materials. Although SMA are known and widely used in various engineering applications for many decades, both understanding and, therefore, modeling of their physical behavior suffer continuous limitations regarding accuracy and performance. The present work reports the results of the properties assessment carried out for the proposed ANN based constitutive model for SMA. As presented, the application of ANN allows to reliably model the hysteretic character of the stress-strain relationship observed by the authors for the experimentally tested SMA material — a wire made of Nitinol. The work is complemented with the results of a study on the influence of an ANN structure and training method on the quality of numerical results. The combined ANN-finite element method code is used to provide solutions for the given dynamic problems. Finally, improvement perspectives regarding SMA constitutive modeling are discussed making a reference to the identified capabilities of the ANN based material model. © 2022, Springer Nature Switzerland AG.

关键词： Neural networks

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Robust Adaptive Sliding Mode control of Virtual Tool-tissue Interaction for Use in Surgery Simulation

Robust Adaptive Sliding Mode Control of Virtual Tool-tissue ...

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RSI/ISM international conference on Robotics and Mechatronics (ICROM)

作者： Zahra Rabieefard Mostafa Rostami Mohammad A. Khosravi Soroush Sadeghnejad Biomedical Engineering Amirkabir University of Technology Tehran Iran

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

control of haptic systems with complex and unstructured environments like the endonasal sinus region, which is used in surgical simulators, puts fundamental challenges forward. This study proposes a robust adaptive sliding mode control technique for adjusting the impedance of a one-degree-of-freedom Falcon robot to a pre-defined nonlinear impedance that is an approximation of a mathematical model of sino-nasal tissue. First, this control scheme is developed for an arbitrary system with n state variables and then applied to the one-degree-of-freedom robot. The Lyapunov theorem demonstrates stability, tracking convergence, and robustness to parametric and nonparametric modeling uncertainty. To validate the effectiveness of the proposed control design, a simulation study and its results are prepared.

关键词： Adaptive systems Target tracking control design Stability analysis Mathematical models Haptic interfaces Impedance Robots Sliding mode control Convergence

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Aggregated Model of Load, Grid Forming, and Grid Following Inverters for an Unbalanced System

Aggregated Model of Load, Grid Forming, and Grid Following I...

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Power and Energy systems Engineering (CPESE), international conference on

作者： Brigitta Wendha Yongseok Lee Jaesung Jung Department of Energy Systems Research Ajou University Suwon South Korea

ISBN: (数字)9798350377439

ISBN: (纸本)9798350377446

The distribution system is known for its inherent unbalanced nature and complex modelling. To decrease complexity when modeling the system to evaluate the dynamic analysis, the aggregated model is developed for load, grid following control inverter-based resource (IBR), and grid forming control IBR. Furthermore, to consider the imbalance of the system, the approximation methodology to estimate the parameters of each phase is proposed for each aggregated model. The proposed approach significantly contributes to the field by offering a simplified yet accurate method for dynamic analysis of unbalanced distribution systems. Validation of the approach is performed through study cases on the IEEE 13-bus system and the known parameters, limited to only the voltage and current of each phase at the connection point and each component’s total power. Furthermore, the dynamic response of the aggregated model is compared to the actual system of IEEE 13 -bus. The results indicate that the unparameterized aggregated model exhibits significant error, while the response of the parameterized aggregated model is similar to that of the actual system, validating the effectiveness of the proposed parameter estimation methodology.

关键词： Dynamic response Analytical models Parameter estimation Grid following Power system dynamics Dynamic scheduling control systems Inverter-based resource Load modeling Grid forming

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