Abstract
The chapter gives a background information on decision support systems and introduces a framework for building dynamic, adaptive decision support systems that evolve their structure and improve their knowledge-base through a continuous learning from data and through interaction with the environment. Such decision support systems use evolving connectionist systems (ECOS) — adaptive learning neural network models, thus their name — evolving connectionist based decision systems ECBDS. Case studies of ECBDS are presented from the areas of finance, economics, and bioinformatics.
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Kasabov, N. (2003). Evolving Connectionist-based Decision Support Systems. In: Yu, X., Kacprzyk, J. (eds) Applied Decision Support with Soft Computing. Studies in Fuzziness and Soft Computing, vol 124. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-37008-6_3
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DOI: https://doi.org/10.1007/978-3-540-37008-6_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-53534-5
Online ISBN: 978-3-540-37008-6
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