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  • Open Access

    ARTICLE

    Unleashing the Power of Multi-Agent Reinforcement Learning for Algorithmic Trading in the Digital Financial Frontier and Enterprise Information Systems

    Saket Sarin1, Sunil K. Singh1, Sudhakar Kumar1, Shivam Goyal1, Brij Bhooshan Gupta2,3,4,8,*, Wadee Alhalabi5, Varsha Arya6,7

    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3123-3138, 2024, DOI:10.32604/cmc.2024.051599 - 15 August 2024

    Abstract In the rapidly evolving landscape of today’s digital economy, Financial Technology (Fintech) emerges as a transformative force, propelled by the dynamic synergy between Artificial Intelligence (AI) and Algorithmic Trading. Our in-depth investigation delves into the intricacies of merging Multi-Agent Reinforcement Learning (MARL) and Explainable AI (XAI) within Fintech, aiming to refine Algorithmic Trading strategies. Through meticulous examination, we uncover the nuanced interactions of AI-driven agents as they collaborate and compete within the financial realm, employing sophisticated deep learning techniques to enhance the clarity and adaptability of trading decisions. These AI-infused Fintech platforms harness collective intelligence More >

  • Open Access

    ARTICLE

    Sliding-Mode Control of Unmanned Underwater Vehicle Using Bio-Inspired Neurodynamics for Discrete Trajectories

    Zhigang Deng1,*, Zhenzhong Chu2, Zaman Mohammed Tousif3

    Intelligent Automation & Soft Computing, Vol.26, No.6, pp. 1503-1515, 2020, DOI:10.32604/iasc.2020.010798 - 24 December 2020

    Abstract Trajectory tracking control can be considered as one of the main researches of unmanned underwater vehicles (UUV). The bio-inspired neurodynamics model was used to make the output continuous and smooth for the inflection points to deal with the speed jump of the conventional tracking controller for discrete trajectories. A horizon-plane trajectory tracking control law is designed using the bio-inspired neurodynamics model and sliding-mode method without chattering. Finally, the simulation of the mentioned two methods is compared with the results showing this as effective and feasible. More >

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