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| Identifier |
000425748 |
| Title |
Predicting dynamical chaos with recurrent neural networks |
| Alternative Title |
Προβλέποντας δυναμικό χάος με επανατροφοδοτούμενα νευρωνικά δίκτυα |
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Author
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Βίλλια, Μαρία Μυρτώ Χ.
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Thesis advisor
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Τσιρώνης, Γεώργιος
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| Abstract |
Predicting chaotic phenomena is tempting. And how could it not be when the most
common definition of chaos is unpredictability? In the present study we are attempting
to predict chaotic time series. These time series are produced by two models that
can appear chaotic behaviour; namely the Lorenz and the Hindmarsh-Rose models.
In order to succeed it, we use a machine learning algorithm called Long Short Term
Memory (LSTM). In particular, in Chapter 1 we present the two used chaotic models,
we give the definitions of some basic concepts of chaos, and we provide in detail the
aims of this work. Then, in Chapter 2, we introduce the basic theory of artificial neural
networks and we focus on the recurrent neural networks, where the LSTMs algorithms
are a special category. Finally, in Chapter 3 we present our results and in Chapter
4 their discussion. Analytically, in Chapter 3 we initially study three specific time
series produced by the Lorenz model. The results of our simulations show that there
is a clear relationship between their prediction horizon and their Lyapunov exponents.
At the same time we demonstrate that this relationship although it is not evident, it
depends on the number of training data. Finally, in the same Chapter 3 we demonstrate
a variant of our main method which provides better predictions on extreme chaotic time
series. Obviously, experimenting with chaos is certainly not an easy task. We need to
develop advanced programming and try out many different versions of our algorithm
before reaching our goals. In this context, we study a time series with peculiar behavior
produced by the Hindmarsh-Rose model and we present our most interesting results.
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| Language |
English |
| Subject |
Machine learning |
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Μηχανική μάθηση |
| Issue date |
2019-11-29 |
| Collection
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School/Department--School of Sciences and Engineering--Department of Physics--Post-graduate theses
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Type of Work--Post-graduate theses
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| Permanent Link |
https://elocus.lib.uoc.gr//dlib/e/0/3/metadata-dlib-1573033530-902586-1968.tkl
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| Views |
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