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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331-340 订阅

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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...

引用

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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Gaussian Reinforcement learning: Optimal Tracking control for Uncertain Linear systems

Gaussian Reinforcement Learning: Optimal Tracking Control fo...

引用

data driven control and learning systems (ddcls)

作者： Yiqing Gang Jinna Li School of Information and Control Engineering Liaoning Petrochemical University Fushun China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In this paper, a design method for PID control based on Gaussian reinforcement learning is proposed to find the optimal policy for linear quadratic tracking of an unknown system. Firstly, the mathematical model of the linear quadratic tracking problem with random disturbance is presented, and an analysis of the PID control structure is carried out. Secondly, a Gaussian model predictive control for the unknown model with random disturbances is provided. Finally, a Gaussian Q-learning method is given for the system based on this predictive model. the effectiveness of the proposed method is demonstrated through a numerical example.

关键词： Linear systems learning systems PI control Q-learning Design methodology Predictive models Mathematical models

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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...

引用

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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Adaptive Iterative learning Optimal control for Linear Multi-Agent systems

Adaptive Iterative Learning Optimal Control for Linear Multi...

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data driven control and learning systems (ddcls)

作者： Duhui Chang Yan Geng School of Science Xi'an Polytechnic University Xi'an China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In order to improve tracking performance of a class of linear discrete time-invariant multi-agent systems, an adaptive optimal iterative learning control strategy is designed. To design the control protocol, a parameter adaptive algorithm is constructed to estimate the Markov parameters of the system. then, virtual leader is used instead of expected trajectory. By optimizing the index function and incorporating estimated parameters into the learning process. An adaptive iterative learning optimal control strategy is constructed. through rigorous analysis, the parameter estimation error is bounded, the tracking error is convergent, and the proof process is independent of system parameters. Finally, numerical simulations validate the effectiveness of this designed control strategy.

关键词： Protocols Simulation Optimal control Adaptive algorithms Iterative algorithms Trajectory Topology

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Consensus Tracking data-driven control of Multi-Agent systems based on Matrix S-Lemma Under Noisy data

Consensus Tracking Data-Driven Control of Multi-Agent System...

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data driven control and learning systems (ddcls)

作者： Shuli Tan Xiufeng Zhang Chunxi Yang Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Intelligent Control and Application Kunming Yunnan China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In this paper, the problem of consensus tracking for multi-agent systems in the presence of noise inter-ference is investigated. Unlike the traditional model-based approach, this paper assumes that the dynamics of all agents are unknown and only a portion of the input-state data is available. the offline data contaminated with noise interference is utilized. By introducing the matrix S-lemma, a necessary and sufficient conditions for directly designing consensus tracking controllers from bounded noisy input-state data is given. this method can be applied to large datasets and effectively addresses computational challenges associated with extensive data. Simulation result illustrates the effectiveness of the theoretical analysis.

关键词： learning systems Sufficient conditions Simulation Noise Interference control systems data models

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A Novel Hyperbolic-Secant Reaching Law of Discrete-Time Sliding Mode control

A Novel Hyperbolic-Secant Reaching Law of Discrete-Time Slid...

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data driven control and learning systems (ddcls)

作者： Guoliang Zhou Lingwei Wu School of Intelligent Manufacture Lishui Vocational and Technical College Lishui China School of Intelligent Manufacture Taizhou University Taizhou China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In this paper, a hyperbolic-secant reaching law-based sliding mode control method is presented for uncertain discrete-time controlled systems. the hyperbolic-secant reaching law based on the hyperbolic-secant function and the saturation function is designed to realize the increasing of the reaching speed and the reduction of the chattering. To characterize the tracking performance of the discrete controlled system, the detailed results about the absolute attractive layer bound and the quasi-sliding mode band are given and analyzed. the validity and usefulness of the proposed control approach can be verified by numerical simulation results.

关键词： learning systems Design methodology Switches Numerical simulation Sliding mode control Convergence

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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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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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data-driven Deadbeat control for Energy Storage systems

Data-Driven Deadbeat Control for Energy Storage Systems

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data driven control and learning systems (ddcls)

作者： Yanyu Zhang Pengpeng Li Shun Zhou Xibeng Zhang Yi Zhou School of Artificial Intelligence Henan University Zhengzhou P. R. China Longzihu New energy Laboratory Zhengzhou P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In recent years, Deadbeat control (DBC) has gained recognition as an effective method for controlling Energy Storage systems (ESS). However, traditional DBC often exhibits substantial steady-state errors attributed to inaccurate system modeling and losses. this paper introduces a novel approach employing Deep Reinforcement learning (DRL) for model-based control to accurately generate current references by compensating for power losses. the proposed method is evaluated through simulation results, highlighting its effectiveness in addressing the limitations of traditional DBC.

关键词： learning systems Adaptation models Adaptive systems Software packages Simulation systems modeling Deep reinforcement learning

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