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    A discontinuous recurrent neural network with predefined time convergence for solution of linear programming

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    Date
    2014-12-09
    Author
    Loukianov, Alexander
    Sánchez-Torres, Juan D.
    Sánchez-Camperos, Edgar
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    Description
    The aim of this paper is to introduce a new recurrent neural network to solve linear programming. The main characteristic of the proposed scheme is its design based on the predefined-time stability. The predefined-time stability is a stronger form of finite-time stability which allows the a priori definition of a convergence time that does not depend on the network initial state. The network structure is based on the Karush-Kuhn-Tucker (KKT) conditions and the KKT multipliers are proposed as sliding mode control inputs. This selection yields to an one-layer recurrent neural network in which the only parameter to be tuned is the desired convergence time. With this features, the network can be easily scaled from a small to a higher dimension problem. The simulation of a simple example shows the feasibility of the current approach.
    Consejo Nacional de Ciencia y Tecnología
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    © ITESO, Universidad Jesuita de Guadalajara 2022

    Sistemas de Información de la Dirección de Información Académica, DIA
    Biblioteca "Dr. Jorge Villalobos Padilla, S.J."

    Contact Us | Send Feedback