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检索条件"任意字段=13th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2024"

共 716 条记录，以下是321-330 订阅

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Employing Deep learning Algorithms for Real-Time Analysis and Prediction of Financial Markets and Investment Strategies 13

Employing Deep Learning Algorithms for Real-Time Analysis an...

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13th ieee International conference on Communication systems and Network Technologies, CSNT 2024

作者： Rajendran, Naveen Kumar Kumari, Sweta Pandey, Vijay Kumar Faculty of Engineering and Technology Department of Aerospace Engineering Karnataka Bangalore India Atlas SkillTech University Department of Isme Maharashtra Mumbai India Vivekananda Global University Department of Mechanical Engineering Jaipur India

ISBN: (纸本)9798350305463

A new age of data-driven decision-making has begun with the incorporation of deep learning algorithms into the domain of financial markets and investment strategies. A summary of our thorough investigation of this groundbreaking convergence is given in this abstract, which covers all the important points from the introduction to the techniques used, the outcomes attained, and the final observations. the introduction lays the groundwork for the paradigm change in finance, which has enabled real-time analysis and prediction via the use of deep learning models. the complexities of financial markets, characterized by high-dimensional time series data and non-linearities, have traditionally presented difficulties for conventional statistical techniques. With the use of deep learning techniques, particularly Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs), it is now possible to anticipate stock prices, exchange rates, and other financial variables with greater accuracy. In comparison tests, the Moving Average Convergence Divergence (MACD) approach yielded a Sharpe ratio of 1.8, whereas LSTM showed a ratio of 2.5. In a similar vein, the Transformer model outperformed the Moving Average (MA) technique, with a Sharpe ratio of 3.0 against 1.5. the main conclusions from this investigation are summarized in the conclusion. Although there are still issues with computing needs and model interpretability, deep learning in banking has the potential to revolutionize the industry. Deep learning algorithms are redefining investing techniques and enabling financial professionals to better traverse the complexity of the financial world. © 2024 ieee.

关键词： Forecasting

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Gain-varying P-type ILC for Nonlinear Multi-agent systems against FDI Attacks

Gain-varying P-type ILC for Nonlinear Multi-agent Systems ag...

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data driven control and learning systems (ddcls)

作者： Huiming Peng Na Lin Ronghu Chi School of Automation & Electronic Engineering Qingdao University of Science & Technology Qingdao P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this work studies the consensus learning control problem of nonlinear multi-agent systems (MASs) under false data injection (FDI) attacks. Applying the attacked output and consensus error information to the control law and the parameter updating algorithm effectively reduces the impact of FDI attacks. the provided control scheme, which is entirely data-driven and more suited for real-world industrial applications, makes no use of any model knowledge from the controlled MASs. In addition, compared with the P-type ILC method, the variable gain effectively improves the robustness of the presented GV-PILC scheme to the external uncertainties. the simulation results verify the effectiveness of the proposed method by comparing with fixed gain P-type ILC.

关键词： learning systems Uncertainty Simulation control systems Robustness Gain Multi-agent systems

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Advancements in Waste Segregation through Machine learning and Integrating AI for Sustainable Waste Management 13

Advancements in Waste Segregation Through Machine Learning a...

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13th International conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Kumar, Munish Kumar, Sandeep Luhach, Ashish Kr. Tiwari, Ankita Etm Research Institute Bahadurgarh India School of Computer Science and Artificial Intelligence Sr University Warangal India The Png University of Technology Department of Electrical & Communication Engineering Papua New Guinea

ISBN: (纸本)9798350380583

Management of waste is very important for sustainable development, mostly in urban areas where population growth and industrial activity are on the rise leading to the generation of huge volume. Proper segregation and collection of waste are essential because it helps in recycling and reduce the amount that piles up as a dump, thus saving several resources. this paper reviews the state of the art in waste segregation methodologies and how different approaches are employed to segregate wastes using machine learning algorithms and robotics use-case scenarios with Internet of things (loT) devices. this paper offers a systematic review synthesising the key elements of segregation in recent literature, comparing datasets for research and examining various ways to measure it. In addition, we suggest improvements to existing systems by the incorporation of multi-modal data processing and real-time adaptive methods. Our results indicate that there have been some major advances in terms of graph processing, but building and deploying a real-time system is still too difficult. the paper concludes with recommendations for future research directions, emphasizing the need for more robust datasets and the integration of AI-driven adaptive systems for dynamic waste management. © 2024 ieee.

关键词： Adversarial machine learning

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Asynchronous data-driven control for Discrete-Time Switched systems With Average Dwell Time

Asynchronous Data-Driven Control for Discrete-Time Switched ...

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data driven control and learning systems (ddcls)

作者： Guandi Wang Qixin Chen Yanzheng Zhu College of Mechanical Engineering and Automation Huaqiao University Fujian China College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

the asynchronous data-driven control problem is concerned for discrete-time switched systems with average dwell time. For a class of unknown switched systems, a data-driven approach is employed to parameterize the controller directly from the input-state data of the system, without relying on the precise mathematical model. Furthermore, a data-driven representation of the closed-loop system is proposed to cope with the asynchronous behavior between subsystems and controllers. Stability conditions in the form of linear matrix inequalities are established based on the Lyapunov theory and the average dwell time method, and then an asynchronous data-driven controller design is implemented. Finally, the feasibility and advantage of the proposed approach are verified through numerical examples.

关键词： Switched systems Asymptotic stability Sufficient conditions Noise Switches Stability analysis Mathematical models

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Opportunities of Gen AI in the Banking Industry with regards to the AI Act, GDPR, data Act and DORA 13

Opportunities of Gen AI in the Banking Industry with regards...

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13th Mediterranean conference on Embedded Computing (MECO)

作者： Botunac, Ive Parlov, Natalija Bosna, Jurica Univ Rijeka Fac Informat & Digital Technol Rijeka Croatia Univ Zadar Zadar Croatia APICURA Zagreb Croatia Univ Zadar Dept Econ Zadar Croatia

ISBN: (纸本)9798350387568;9798350387575

Generative Artificial Intelligence (Gen AI) stands at the forefront of the banking sector's technological revolution, promising enhancements in decision-making, risk management, and customer interaction. this paper examines Gen AI's potential to inject innovation and efficiency into banking services, with an estimated value addition of up to $340 billion annually. Grounded in advancements in NLP through Transformer architecture and evolving GPT models, Gen AI's applications in the banking industry are extensive. they range from personalizing customer service with AI-driven chatbots to revolutionizing credit scoring and trading strategies. However, alongside these opportunities, the paper addresses the significant challenges of regulatory compliance, ethical data usage, and the technical integration of AI systems. With the impending release of the EU's AI Act and existing GDPR and DORA, financial institutions must strategize to align with new standards while harnessing Gen AI's capabilities for process optimization and enhanced service delivery. the role of international standards such as ISO/IEC 42001:2023, ISO 31000:2018, ISO/IEC 23894:2023, NIST AI 600-1 and ISO/IEC 23053:2022 is considered to be beneficial in establishing a common framework for managing AI systems, ensuring data integrity and promoting transparency. By adopting these standards, banks can facilitate compliance across various jurisdictions, enhancing operational consistency and reliability - but certain significant limitations in addressing specific regulatory requirements must be taken into account. the paper concludes that Gen AI's future in banking will be transformative, driven by the industry's need to balance technological innovation with ethical and regulatory requirements and process standardization, which will lead to more transparent, personalized and efficient banking services.

关键词： Artificial Intelligence Gen AI Machine learning Banking AI Act data Act GDPR DORA

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Model-Free Adaptive Cloud Elastic control for Small Nonlinear Agent systems

Model-Free Adaptive Cloud Elastic Control for Small Nonlinea...

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data driven control and learning systems (ddcls)

作者： Lin Zhu Dongsheng Yang Weiwei Che College of Information Science and Engineering Northeastern University Shenyang Liaoning PR China School of Automation Qingdao University Shandong Qingdao PR China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In this paper, for unknown small nonlinear multi-agent(SNMA) systems without large computing capabilities, cloud computing technology is used to calculate control commands and achieve multi-agent consensus control. At the same time, considering the security problem of the network channel between the cloud and the agent, a model-free adaptive cloud elastic control(MFACEC) strategy was proposed. Under the MFACEC control strategy, each agent whose model is unknown is able to complete consensus tracking. Finally, simulation results show the effectiveness of the algorithm.

关键词： learning systems Cloud computing Adaptation models Adaptive systems Computational modeling Simulation Consensus control

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Dynamic Encoding-Decoding-Based Quantized Iterative learning control

Dynamic Encoding-Decoding-Based Quantized Iterative Learning...

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data driven control and learning systems (ddcls)

作者： Niu Huo Dong Shen School of Mathematics Renmin University of China Beijing P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

the tracking performance of linear discrete-time systems under quantized iterative learning control is investigated in this paper. An encoding-decoding mechanism is utilized to process the output of the system that is transmitted over the network channel. the encoding method includes the quantization procedure, which employs a uniform quantizer with finite quantization levels. the scaling parameters in the encoder and decoder are formulated to dynamically adjust based on the input of the quantizer. the learning control scheme is designed based on the output generated by the decoder. the results demonstrate that the system attains error-free tracking performance. the theoretical findings of this research are confirmed by a numerical simulation.

关键词： Discrete-time systems learning systems Quantization (signal) Numerical simulation control systems Encoding Decoding

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Containment control via Iterative learning of Fractional-order Singular Multi-agent systems with Initial State learning

Containment Control via Iterative Learning of Fractional-ord...

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data driven control and learning systems (ddcls)

作者： Guangxu Wang Xingyu Zhou Xisheng Dai School of Electrical and Automation Nantong University Nantong China School of Zhang Jian Nantong University Nantong China School of Automation Guangxi University of Science and Technology Liuzhou China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In a category of nonlinear fractional-order singular multi-agent systems (MASs) with initial state learning scheme, the closed-loop $\mathscr{D}^{\alpha}$ -type iterative learning control protocol is introduced to address the containment control problem for the aforementioned MASs. Subsequently, in terms of the fractional-order calculus and the generalized Gronwall inequality, sufficient conditions are derived for the learning convergence of containment errors and initial containment errors in such MASs. Lastly, a numerical illustration is given to validate the efficacy of the closed-loop $\mathscr{D}^{\mathfrak{a}}$ -type iterative learning containment control protocol.

关键词： learning systems Sufficient conditions Protocols control systems Calculus Fractional calculus Multi-agent systems

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Iterative learning Fault Estimation for Stochastic Time-varying systems

Iterative Learning Fault Estimation for Stochastic Time-vary...

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data driven control and learning systems (ddcls)

作者： Jiawen Tang Wenli Zhang Anning Liu Jiantao Shi College of Electrical Engineering and Control Science Nanjing Tech University Nanjing China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

the article investigates the fault estimation problem for a class of stochastic time-varying systems based on iterative learning methods. Unlike most traditional works, the system investigated in this paper is affected by disturbances, noises and initial state deviations. A type of iterative learning fault estimator is designed utilizing the historical output estimation errors for receiving the fault information of the system. Furthermore, the existence condition is analyzed in order to meet boundedness with respect to the A-norm of fault estimation errors based on the proposed iterative learning scheme. At last, a case study is applied for verify the effectiveness of the studied scheme.

关键词： learning systems Estimation error Noise Observers control systems Iterative methods Time-varying systems

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Markov Parameters Estimation and data-driven Iterative learning control for LDTV systems

Markov Parameters Estimation and Data-Driven Iterative Learn...

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data driven control and learning systems (ddcls)

作者： Chuyang Liu Xiaoe Ruan School of Science Chang'an University Xi'an China School of Mathematics and Statistics Xi'an Jiaotong University Xi'an China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In this paper, a kind of Markov parameters estimation and data-driven iterative learning control scheme is constructed for a class of linear discrete-time-varying systems. Firstly, a linear combination of the predicted system output error energy and estimation compensation energy is designed as the performance index function. through algebraic operations, the estimation formula for the Markov matrix that minimizes the performance index function is explicitly solved. Simultaneously, within the framework of norm-optimal iterative learning control where the controller is regarded as an optimization variable, the updating rule for control input is formulated. Substituting the Markov matrix with its estimation in the control input updating rule, a data-driven iterative learning control scheme is established. Numerical experiments demonstrate that both the estimation error of the system Markov parameters and the tracking error of the system output monotonically decrease along the iteration axis. Moreover, a smaller estimation ratio leads to a faster decrease in the estimation error, and a smaller learning ratio results in a faster decrease in the tracking error.

关键词： learning systems Estimation error Parameter estimation Optimization methods control systems Matrices Performance analysis

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