Abstract
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.
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Brandejsky, T. (2013). Model Identification from Incomplete Data Set Describing State Variable Subset Only – The Problem of Optimizing and Predicting Heuristic Incorporation into Evolutionary System. In: Zelinka, I., Chen, G., Rössler, O., Snasel, V., Abraham, A. (eds) Nostradamus 2013: Prediction, Modeling and Analysis of Complex Systems. Advances in Intelligent Systems and Computing, vol 210. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00542-3_19
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DOI: https://doi.org/10.1007/978-3-319-00542-3_19
Publisher Name: Springer, Heidelberg
Print ISBN: 978-3-319-00541-6
Online ISBN: 978-3-319-00542-3
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