Model Identification from Incomplete Data Set Describing State Variable Subset Only - The Problem of Optimizing and Predicting Heuristic Incorporation into Evolutionary System

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21260%2F13%3A00207320" target="_blank" >RIV/68407700:21260/13:00207320 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-00542-3_19" target="_blank" >http://dx.doi.org/10.1007/978-3-319-00542-3_19</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-00542-3_19" target="_blank" >10.1007/978-3-319-00542-3_19</a>

Result language
angličtina
Original language name
Model Identification from Incomplete Data Set Describing State Variable Subset Only - The Problem of Optimizing and Predicting Heuristic Incorporation into Evolutionary System
Original language description
Presented paper describes the application of evolutionary system GPA-ES in difficult task of chaotic system symbolic regression from incomplete training data set describing only some of model variables. The algorithm uses many heuristics which are described below and which will be subject of future development. The first test of algorithm was applying the Lorenz attractor system data, where only the original system x and y variable data were used and z variable data were estimated
Czech name
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Czech description
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Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Nostradamus 2013: Prediction, Modeling and Analysis of Complex Systems
ISBN
978-3-319-00541-6
ISSN
2194-5357
e-ISSN
—
Number of pages
9
Pages from-to
181-189
Publisher name
Springer
Place of publication
Heidelberg
Event location
Ostrava
Event date
Jun 3, 2013
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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