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Books > Computing & IT > Applications of computing > Artificial intelligence
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Deep Learning in Multi-step Prediction of Chaotic Dynamics - From Deterministic Models to Real-World Systems (Paperback, 1st ed. 2021)
Loot Price: R1,473
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Deep Learning in Multi-step Prediction of Chaotic Dynamics - From Deterministic Models to Real-World Systems (Paperback, 1st ed. 2021)
Series: SpringerBriefs in Applied Sciences and Technology
Expected to ship within 18 - 22 working days
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The book represents the first attempt to systematically deal with
the use of deep neural networks to forecast chaotic time series.
Differently from most of the current literature, it implements a
multi-step approach, i.e., the forecast of an entire interval of
future values. This is relevant for many applications, such as
model predictive control, that requires predicting the values for
the whole receding horizon. Going progressively from deterministic
models with different degrees of complexity and chaoticity to noisy
systems and then to real-world cases, the book compares the
performances of various neural network architectures (feed-forward
and recurrent). It also introduces an innovative and powerful
approach for training recurrent structures specific for
sequence-to-sequence tasks. The book also presents one of the first
attempts in the context of environmental time series forecasting of
applying transfer-learning techniques such as domain adaptation.
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