Abstract
Reconstruction of a dynamical system from data is in one sense easy: if we have enough parameters then we can exactly reproduce the original observations, and what could be better? The answer is, of course, that a lot of things could be better. We are usually interested in cases which are not quite the same as the observations, and if we try too hard to fit the data then we are probably fitting noise and making it likely that our predictions will be bad - maybe very bad. Even without noise, if our model is based on inaccurate assumptions, the results can be poor. In this chapter we discuss what it means to build a model, and describe some experience with a particular approach, i.e., the application of minimum description length to radial basis neural networks.
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Mees, A.I., Judd, K. (1996). Parsimony in Dynamical Modeling. In: Kravtsov, Y.A., Kadtke, J.B. (eds) Predictability of Complex Dynamical Systems. Springer Series in Synergetics, vol 69. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-80254-6_7
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DOI: https://doi.org/10.1007/978-3-642-80254-6_7
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