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Neural learning of chaotic dynamics

✍ Scribed by Gustavo Deco; Bernd Schürmann


Publisher
Springer US
Year
1995
Tongue
English
Weight
271 KB
Volume
2
Category
Article
ISSN
1370-4621

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Learning persistent dynamics with neural
✍ H.A Ceccatto; H.D Navone; H Waelbroeck 📂 Article 📅 1998 🏛 Elsevier Science 🌐 English ⚖ 296 KB

Persistence is one of the most common characteristics of real-world time series. In this work we investigate the process of learning persistent dynamics by neural networks. We show that for chaotic times series the network can get stuck for long training periods in a trivial minimum of the error fun