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

共 12248 条记录，以下是1251-1260 订阅

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Integrating Deep Neural Networks for Hybrid modeling of complex Chemical Processes: Estimation of Spatiotemporally Varying Parameters in Moving Boundary problems

Integrating Deep Neural Networks for Hybrid Modeling of Comp...

引用

American control conference (ACC)

作者： Silabrata Pahari Parth Shah Joseph Sang-Il Kwon Artie McFerrin Department of Chemical Engineering

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

Hybrid modeling has gained substantial traction due to its capacity to combine machine learning techniques with the preservation of the model's physical essence. While these hybrid models have primarily tackled temporal processes governed by ordinary differential equations (ODEs), the complexity of many real-world systems - mirroring diverse physical processes - surpasses this scope. This work examines hybrid modeling methods extensively applied to an intricate biological system governed by the reaction-diffusion equation, which is a partial differential equation (PDE), targeting the challenges arising from latent chemical mechanisms. The methodology introduces a hybrid modeling architecture that synergizes neural networks and mathematical methods to estimate varying parameters across both space and time within a class of moving boundary problems like reaction-diffusion, all while upholding boundary conditions. The training of the model is done using a backpropagation algorithm that efficiently updates these parameters while ensuring numerical stability. The hybrid model is applied to Reaction-Diffusion models, and the results discuss the accurate estimation of spatiotemporally varying diffusivity, and temporally varying cell proliferation rate and cell carrying density.

关键词： Training Microprocessors Estimation Computer architecture Machine learning Predictive models Mathematical models

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An Improved Time Series Model

An Improved Time Series Model

引用

complex systems and Intelligent Science (CSIS-IAC), international Annual conference on

作者： Zhi Liu Zhiyi Zhang Department of Basic Shenyang University of Technology Liaoyang China Faculty of Electrical and Control Engineering Liaoning Technical University Huludao China

ISBN: (数字)9798331504755

ISBN: (纸本)9798331504762

Fuzzy time series have demonstrated considerable prowess in addressing nebulous and indeterminate data. In order to enhance the predictive accuracy of the model, this research integrates fixed-point theory with time series analysis, introducing the concepts of fractional functions, inverse fractional functions, and predictive functions, while also demonstrating the convergence of these predictive functions. Building on this foundation, the paper further refines the theory of fuzzy time series by establishing a fixed-point-based fuzzy time series model, which has been applied to forecast the enrollment numbers at the University of Alabama and the sales volume of new energy vehicles in China. The prediction results showed the models high accuracy, robust theoretical foundations, and broad applicability.

关键词： Training Solid modeling Accuracy Time series analysis Buildings Predictive models Stability analysis complex systems Testing Convergence

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Comparative Analysis of the Rate of Convergence of the Methods of Gradient Descent and Natural Gradient Descent in Regression Analysis problems

Comparative Analysis of the Rate of Convergence of the Metho...

引用

international conference on control systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Alexei Tyurin Lipetsk State Technical University Lipetsk Russia

This article provides a comparative analysis of gradient descent and natural gradient descent in its own implementation in terms of accuracy and convergence rate on various data sets. The results of the experiments obtained make it possible to compare the effectiveness of both methods both in terms of classical metrics for evaluating mathematical models, and in terms of convergence rate and learning time. The findings of the study help to choose the optimal optimization method, depending on the requirements for accuracy and speed of work when building mathematical models

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Reset control to Suppress Chaotic Disturbance for Pneumatic control valve

Reset Control to Suppress Chaotic Disturbance for Pneumatic ...

引用

international conference on control, Automation and systems ( ICCAS)

作者： Masataka Iwai International Professional University of Technology in Osaka Kobe Japan

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

In increasingly complex systems, it is becoming increasingly difficult to accurately identify the location of failures and the extent of their effects. Chaos engineering is a method to improve the reliability of the system by causing failures to reveal the problems. In this study, we propose a method of reset control that suppresses the failure of a pneumatic control valve by simulating the failure of the valve, incorporating the concept of chaos engineering. In this study, in order to study a new control method for the control parts of the positioner, simulations are shown in which a chaotic signal is superimposed as a simulated disturbance on the input signal of a mathematical model of a pneumatic control valve with an electronic positioner. As an appropriate control method for control parts of the positioner, we apply the reset control, which autonomously generates pulses from the control deviation as a feedback control. The parameters of the reset control are adjusted for this model, and the simulation results show this reset control with these parameters can suppress failures caused by the superimposition of simulated chaotic signals.

关键词： Chaos Simulation Process control Position measurement Valves Reliability engineering control systems Mathematical models Feedback control Data communication

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NeuroPhysNet: A Novel Hybrid Neural Network Model for Enhanced Prediction and control of Cyber-Physical systems 19th

NeuroPhysNet: A Novel Hybrid Neural Network Model for Enhanc...

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19th international conference on Mathematical modeling and Simulation of systems, MODS 2024

作者： Dolhopolov, Serhii Honcharenko, Tetyana Chernyshev, Denys Kyiv National University of Construction and Architecture 31 Air Force Avenue Kyiv03037 Ukraine

ISBN: (纸本)9783031907340

This paper introduces NeuroPhysNet, a novel hybrid neural network architecture designed for enhanced prediction and control of cyber-physical systems (CPS). The increasing complexity and dynamic nature of CPS present significant challenges in modeling and control, often stretching the limits of traditional approaches. NeuroPhysNet addresses these challenges by seamlessly integrating deep learning techniques with physics-based modeling, offering a robust framework that leverages both data-driven insights and domain-specific knowledge. The architecture comprises three main components: a Deep Neural Network (DNN) module for capturing complex non-linear relationships, a Physical Model Integration (PMI) module for incorporating domain-specific equations, and an Adaptive Fusion (AF) module for dynamically combining outputs based on current system states. We evaluate NeuroPhysNet across multiple CPS domains, including smart grid energy management, autonomous vehicle control, and industrial process control. Results demonstrate significant improvements in prediction accuracy, physical consistency, and generalization capabilities compared to state-of-the-art baseline models. NeuroPhysNet achieves up to 15% improvement in mean squared error and a 20% increase in physical consistency scores across tested domains. This research contributes to the growing field of physics-informed machine learning, offering a versatile approach for enhancing the performance and reliability of complex cyber-physical systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Physical addresses

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A Robust Flight controller Design: Investigating Guidance Failures Near TSS Heliport in Challenging Wind Conditions

A Robust Flight Controller Design: Investigating Guidance Fa...

引用

international conference on Unmanned Aircraft systems (ICUAS)

作者： Gulnihal Kucuksayacigil Shawn Keshmiri Mounir Chrit Department of Aerospace Engineering University of Kansas Lawrence KS USA Department of At-mospheric Sciences University of North Dakota Grand Forks ND USA

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

With the imminent integration of Advanced Air Mobility (AAM) into the national airspace, ensuring the robustness of flight controllers in spatially congested metropolitan areas and in the presence of external disturbances is of paramount importance. The complex interaction between atmospheric turbulence and tall buildings further exacerbates the effects of wind disturbances, posing significant safety challenges to aircraft stability and trajectory tracking. This study employs the high-fidelity urban wind field model using Computational Fluid Dynamics (CFD) which captures wind variations in urban environments. This model quantifies wind shear intensity and vorticity distributions, which are critical factors affecting flight performance. The failure of an autonomous fixed-wing aircraft to maintain its intended flight path within permissible deviation limits under extreme wind conditions is investigated. To address these challenges, a robust flight control system is developed to enhance trajectory tracking performance and mitigate the adverse effects of wind on the path following. The proposed controller is designed to ensure reliable operation despite the unpredictability of urban wind fields, contributing to safer and more resilient autonomous flight operations in complex metropolitan airspaces.

关键词： Trajectory tracking Atmospheric modeling Computational modeling Computational fluid dynamics Urban areas Stability analysis Robustness Safety Aircraft Aerospace control

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Photovoltaic Inverter Frequency Coupling Admittance modeling and Characteristic Analysis Based on Simplified modeling Methods

Photovoltaic Inverter Frequency Coupling Admittance Modeling...

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international conference on Power and Energy Technology (ICPET)

作者： Yunyi Zhu Lucheng Hong Minghe Wu Lin Han School of Electrical Engineering Southeast University Nanjin China

ISBN: (数字)9798350375855

ISBN: (纸本)9798350375862

With the large-scale integration of distributed Photovoltaics (PV) and other renewable energy sources into the distribution grid, the proportion of grid-following inverters and nonlinear power electronic devices is continuously increasing. Current modeling of grid-following inverters presents two main issues: first, the derivation process is lengthy and error-prone, making it unsuitable for modeling complex control systems; second, the lack of precision can lead to incorrect stability assessments. To address these issues, this paper proposes a simplified admittance modeling method for PV inverters that considers frequency coupling between the AC side and the AC-DC interface. Based on this method, a positive and negative sequence admittance model for a three-phase single-stage grid-tied PV inverter is established, and the admittance analytical model is validated through simulations using PSCAD/EMTDC. Additionally, the impacts of the proportional and integral coefficients of the outer DC voltage loop and the current inner loops on impedance characteristics are analyzed in detail.

关键词： Couplings Photovoltaic systems Analytical models Renewable energy sources Accuracy Grid following Process control Stability analysis Admittance Time-domain analysis

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Computational Range Maximization Under Current Constraints for Heavy-Duty Electric Vehicles

Computational Range Maximization Under Current Constraints f...

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international conference on Electrical systems for Aircraft, Railway, Ship Propulsion and Road Vehicles & international Transportation Electrification conference (ESARS-ITEC)

作者： Eero Immonen Computational Engineering and Analysis Research Group Turku University of Applied Sciences Turku Finland

ISBN: (数字)9798350373905

ISBN: (纸本)9798350373912

In this article, we study model-based Heavy-Duty Electric Vehicle (HDEV) range maximization under bounds for the discharge current, as imposed by the onboard Battery Management System (BMS). We present a computational optimization framework that facilitates design of optimal controls for battery Equivalent Circuit Models (ECM) of different fidelities. The objective is to compare the different model predictions and assess the efficacy of simple control laws relative to more complex ones that attempt to exploit the battery's modeled characteristics. We confine our attention to windless flat-track conditions to isolate the effect of the Battery Energy System (BES). In numerical simulations, we focus on Lithium-Titanate Oxide (LTO) batteries, which have been found prime candidates for HDEV applications. The results show that the maximum range predictions, and the associated optimal control policies, depend substantially on the ECM utilized. However, for all models, good maximal range predictions are also obtained by using simple constant controls. This is consistent with Keller's theory of optimal pacing.

关键词： Computational modeling Force Optimal control Transportation Predictive models Electric vehicles Batteries Numerical models Integrated circuit modeling Optimization

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Methods of Congestion control in Wired Networks with Reinforcement Learning-A Review 2

Methods of Congestion Control in Wired Networks with Reinfor...

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2nd international conference on Innovative Practices in Technology and Management, ICIPTM 2022

作者： Lakshmi, Mettu Jhansi Babuarrama, Mahesh KoneruLakshmaiah Education Foundation Department Of Cse Hyderabad India

ISBN: (纸本)9781665466431

In a TCP/IP network, the TCP congestion control system (CC) ensures that network resources are shared efficiently and fairly between its users. Previously, TCP CC systems have been designed to cable hard from predefined behaviour to particular network control signal. However, as networks become more complex and competitive, the optimal feedback action mapping the invention of this analysis will be difficult to develop, and the contribution of this study is the thorough description of and comparison of the congestion control (CC) approaches. Comparatively to standard CC algorithms, which are primarily rule-based, the capacity to learn from previous knowledge is extremely valuable. According to the research, RL is a major trend among learning-based CC algorithms. In the paper we discuss the efficiency of CC algorithms on an RL basis and present current issues with CC algorithms on an RL basis. We describe the problems and dynamics of CC algorithms based on learning. © 2022 IEEE.

关键词： Reinforcement learning

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An Evaluation of Internet of Nano-Things Simulators 6

An Evaluation of Internet of Nano-Things Simulators

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6th international conference on Computer Science and Engineering, UBMK 2021

作者： Sahin, Emre Dagdeviren, Orhan Akkas, Mustafa Alper Management Information Systems Pamukkale University Denizli Turkey International Computer Institute Ege University Izmir Turkey Computer Engineering Bolu Abant Izzet Baysal University Bolu Turkey

ISBN: (纸本)9781665429085

Nowadays, distributed systems consisting of a large number of simple devices are used to perform complex operations rather than advanced devices alone. Particularly, networked systems of nanoscale devices are called Internet of Nano-Things (IoNT). This new field has emerged with new problems to be solved where simulation of nanonetworks is one of these problems. In this study, Electromagnetic (EM) communication-based nanonetwork simulators used in IoNT applications are examined in detail to determine the advantages and their drawbacks. Different than the related studies, our contribution is a detailed and up-to-date roadmap for simulator selection for researchers working in different layers of nanonetwork applications such as channel modeling, antenna design, modulation methods, medium access control (MAC) protocols, and routing algorithms. © 2021 IEEE

关键词： Wireless networks

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