2004 | OriginalPaper | Buchkapitel
The Synergy between Classical and Soft-Computing Techniques for Time Series Prediction
verfasst von : Ignacio Rojas, Fernando Rojas, Héctor Pomares, Luis Javier Herrera, Jesús González, Olga Valenzuela
Erschienen in: MICAI 2004: Advances in Artificial Intelligence
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
A new method for extracting valuable process information from input-output data is presented in this paper using a pseudo-gaussian basis function neural network with regression weights. The proposed methodology produces dynamical radial basis function, able to modify the number of neuron within the hidden layer. Other important characteristic of the proposed neural system is that the activation of the hidden neurons is normalized, which, as described in the bibliography, provides better performance than non-normalization. The effectiveness of the method is illustrated through the development of dynamical models for a very well known benchmark, the synthetic time series Mackey-Glass.