✦ LIBER ✦

Reinforcement learning in random neural networks for cascaded decisions

✍ Scribed by Ugur Halici

Publisher: Elsevier Science
Year: 1997
Tongue: English
Weight: 771 KB
Volume: 40
Category: Article
ISSN: 0303-2647
DOI: 10.1016/0303-2647(96)01633-4

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Learning in the feed-forward random neur

Learning in the feed-forward random neural network: A critical review

✍ Michael Georgiopoulos; Cong Li; Taskin Kocak 📂 Article 📅 2011 🏛 Elsevier Science 🌐 English ⚖ 959 KB

Lower bound for connectivity in local-le

Lower bound for connectivity in local-learning neural networks

✍ Yaser S Abu-Mostafa 📂 Article 📅 1988 🏛 Elsevier Science 🌐 English ⚖ 457 KB

Reinforcement-learning-based self-organi

Reinforcement-learning-based self-organisation for cell configuration in multimedia mobile networks

✍ Liao, Ching-Yu ;Yu, Fei ;Leung, Victor C. M. ;Chang, Chung-Ju 📂 Article 📅 2005 🏛 John Wiley and Sons 🌐 English ⚖ 201 KB

Artificial Neural Networks for Modeling

Artificial Neural Networks for Modeling Knowing and Learning in Science

✍ Wolff-Michael Roth 📂 Article 📅 2000 🏛 John Wiley and Sons 🌐 English ⚖ 94 KB 👁 2 views

Recent neurobiological evidence suggests that environmentally derived activity plays a central role in regulating neuronal growth and neuronal connectivity. Artificial neural networks with distributed representations display many features of knowing and learning that are known from biological intell

A fuzzy reinforcement learning approach

A fuzzy reinforcement learning approach for self-optimization of coverage in LTE networks

✍ Rouzbeh Razavi; Siegfried Klein; Holger Claussen 📂 Article 📅 2010 🏛 Institute of Electrical and Electronics Engineers 🌐 English ⚖ 802 KB

A new perspective in learning pattern ge

A new perspective in learning pattern generation for teaching neural networks

✍ G.-P.K. Economou; D. Lymberopoulos; K. Spiropoulos; P.D. Goumas 📂 Article 📅 1999 🏛 Elsevier Science 🌐 English ⚖ 175 KB

This article deals with the Learning Patterns (LPs)' generation, a major aspect of Feed-Forward Artificial Neural Networks (FANNs)' learning process. Currently, more work is done to understand the mechanisms and improve the speed, learning accuracy, and implementation features of FANNs' teaching alg