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Learning Automata And Their Applications To Intelligent Systems at Meripustak

Learning Automata And Their Applications To Intelligent Systems by Junqi Zhang, John Wiley

Books from same Author: Junqi Zhang

Books from same Publisher: John Wiley

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  • General Information  
    Author(s)Junqi Zhang
    PublisherJohn Wiley
    Edition1st Edition
    ISBN9781394188499
    Pages272
    BindingHardcover
    LanguageEnglish
    Publish YearNovember 2023

    Description

    John Wiley Learning Automata And Their Applications To Intelligent Systems by Junqi Zhang

    Comprehensive guide on learning automata introducing two variants to accelerate convergence and computational update speedLearning Automata and Their Applications to Intelligent Systems provides a comprehensive guide on learning automata from the perspective of principles algorithms improvement directions and applications. The text introduces two variants to accelerate the convergence speed and computational update speed respectively these two examples demonstrate how to design new learning automata for a specific field from the aspect of algorithm design to give full play to the advantage of learning automata.As noisy optimization problems exist widely in various intelligent systems this book elaborates on how to employ learning automata to solve noisy optimization problems from the perspective of algorithm design and application.The existing and most representative applications of learning automata include classification clustering game knapsack network optimization ranking and scheduling. They are well-discussed. Future research directions to promote an intelligent system are suggested.Written by two highly qualified academics with significant experience in the field Learning Automata and Their Applications to Intelligent Systems covers such topics as Mathematical analysis of the behavior of learning automata along with suitable learning algorithmsTwo application-oriented learning automata one to discover and track spatiotemporal event patterns and the other to solve stochastic searching on a lineDemonstrations of two pioneering variants of Optimal Computing Budge Allocation (OCBA) methods and how to combine learning automata with ordinal optimizationHow to achieve significantly faster convergence and higher accuracy than classical pursuit schemes via lower computational complexity of updating the state probabilityA timely text in a rapidly developing field Learning Automata and Their Applications to Intelligent Systems is an essential resource for researchers in machine learning engineering operation and management. The book is also highly suitable for graduate level courses on machine learning soft computing reinforcement learning and stochastic optimization.



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