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AppART: An ART Hybrid Stable Learning Neural Network for Universal Function Approximation

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Hybrid Information Systems

Part of the book series: Advances in Soft Computing ((AINSC,volume 14))

Abstract

This work describes AppART, an ART—based low parameterized neural model that incrementally approximates continuous—valued multidimensional functions from noisy data using biologically plausible processes. AppART performs a higher—order Nadaraya—Watson regression and can be interpreted as a fuzzy system. Some benchmark problems are solved in order to study AppART from an application point of view and to compare its results with the ones obtained from other models.

The authors wish to thank the Dipartimento di Matematica e Informatica of the Università degli Studi di Udine for its support on the conception and elaboration of this work.

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Author information

Authors and Affiliations

  1. Dipartimento di Matematica e Informatica, Università degli Studi di Udine, Udine, Italy

    Luis Martí & Alberto Policriti

  2. Facultad de Matemática y Computación, Universidad de La Habana, La Habana, Cuba

    Luis Martí & Luciano García

Authors
  1. Luis Martí
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  2. Alberto Policriti
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  3. Luciano García
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Editor information

Editors and Affiliations

  1. School of Computing and Information Technology, Monash University, Gippsland Campus, 3842, Churchill, VIC, Australia

    Ajith Abraham

  2. Department of Pattern Recognition, Fraunhofer IPK Berlin, Pascalstraße 8-9, 10587, Berlin, Germany

    Mario Köppen

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© 2002 Springer-Verlag Berlin Heidelberg

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Martí, L., Policriti, A., García, L. (2002). AppART: An ART Hybrid Stable Learning Neural Network for Universal Function Approximation. In: Abraham, A., Köppen, M. (eds) Hybrid Information Systems. Advances in Soft Computing, vol 14. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1782-9_9

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  • DOI: https://doi.org/10.1007/978-3-7908-1782-9_9

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-1480-4

  • Online ISBN: 978-3-7908-1782-9

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