This paper investigates the problem of approximating a dynamical system (DS) by a recurrent neural network (RNN) as one extension of the problem of approximating orbits by an RNN. We systematically investigate how an RNN can produce a DS on the visible state space to approximate a given DS and as a
โฆ LIBER โฆ
Reinforcement learning of dynamic behavior by using recurrent neural networks
โ Scribed by Ahmet Onat; Hajime Kita; Yoshikazu Nishikawa
- Publisher
- Springer Japan
- Year
- 1997
- Tongue
- English
- Weight
- 411 KB
- Volume
- 1
- Category
- Article
- ISSN
- 1433-5298
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