Abstract
Multiple models, neural networks, cluster analysis and probabilistic mixtures are prominent examples of situations when complex multi-modal models [1] are built using vast amount of data. Complexity and non-unicity of modified situation imply that resulting description depends heavily on the initial phase of search. The safest repetitive purely random search is mostly inhibited by computational complexity of the addressed task. For this reasons, various techniques have been designed. None of them, to our best knowledge, suits to cases when dynamic models are constructed. The paper describes a novel technique that fills this gap in a promising way. Essentially, the trial description is gradually split whenever there is possibility that a unimodal sub-model hides more modes.
This research was partially supported by EC, grant IST-99-12058, e-mail: school@utia.cas.cz
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
R. Murray-Smith and T. Johansen, Multiple Model Approaches to Modelling and Control. London: Taylor & Francis, 1997.
V. Peterka, “Bayesian system identification” in Trends and Progress in System Identification (P. Eykhoff), Oxford: Pergamon Press, 1981.
M. Kàrný, et al, “Quasi-Bayes estimation applied to normal mixture” in 3rd IEEE Workshop CMP (J. Rojicek et al), UTIA, 1998.
S. Kullback and R. Leibier, “On information and sufficiency” Annals of Mathematical Statistics, vol. 22, 1951
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Wien
About this paper
Cite this paper
Kárný, M., Nedoma, P., Nagy, I., Valečková, M. (2001). Initial Description of Multi-Modal Dynamic Models. In: Kůrková, V., Neruda, R., Kárný, M., Steele, N.C. (eds) Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6230-9_99
Download citation
DOI: https://doi.org/10.1007/978-3-7091-6230-9_99
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83651-4
Online ISBN: 978-3-7091-6230-9
eBook Packages: Springer Book Archive