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Sliding mode-based adaptive learning in dynamical adalines

Abstract:

A sliding mode control strategy is proposed for the synthesis of adaptive learning algorithms in perceptron-based feedforward neural networks whose weights are constituted by first order, time-varying, dynamical systems with adjustable parameters. The approach is shown to exhibit remarkable robustness and fast convergence properties. A simulation example, dealing with an analog signal tracking task, is provided which illustrates the feasibility of the approach.

Año de publicación:

1997

Keywords:

    Fuente:

    scopusscopus

    Tipo de documento:

    Conference Object

    Estado:

    Acceso restringido

    Áreas de conocimiento:

    • Sistema de control
    • Aprendizaje automático
    • Teoría de control

    Áreas temáticas:

    • Ciencias de la computación

    Contribuidores:

    Sira-Ramirez H.Sira-Ramirez H.
    Francklin RivasFrancklin Rivas
    Eliezer Colina-MorlesEliezer Colina-Morles