✦ LIBER ✦

Sigmoidal Function Classes for Feedforward Artificial Neural Networks

✍ Scribed by Pravin Chandra

Book ID: 111602140
Publisher: Springer US
Year: 2003
Tongue: English
Weight: 116 KB
Volume: 18
Category: Article
ISSN: 1370-4621
DOI: 10.1023/b:nepl.0000011137.04221.96

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

An activation function adapting training

An activation function adapting training algorithm for sigmoidal feedforward networks

✍ Pravin Chandra; Yogesh Singh 📂 Article 📅 2004 🏛 Elsevier Science 🌐 English ⚖ 239 KB

Function Estimation by Feedforward Sigmo

Function Estimation by Feedforward Sigmoidal Networks with Bounded Weights

✍ Nageswara S.V. Rao; Vladimir Protopopescu 📂 Article 📅 1998 🏛 Springer US 🌐 English ⚖ 89 KB

Neural Smithing – Supervised Learning in

Neural Smithing – Supervised Learning in Feedforward Artificial Neural Networks

✍ Hammer, Barbara 📂 Article 📅 2001 🏛 Springer-Verlag 🌐 English ⚖ 90 KB

Double-exponential sigmoidal functions f

Double-exponential sigmoidal functions for neural networks

✍ M. Heiss; S. Kampl 📂 Article 📅 1997 🏛 Springer 🌐 English ⚖ 308 KB

A class-modular feedforward neural netwo

A class-modular feedforward neural network for handwriting recognition

✍ Il-Seok Oh; Ching Y. Suen 📂 Article 📅 2002 🏛 Elsevier Science 🌐 English ⚖ 415 KB

Classes of feedforward neural networks a

Classes of feedforward neural networks and their circuit complexity

✍ John S. Shawe-Taylor; Martin H.G. Anthony; Walter Kern 📂 Article 📅 1992 🏛 Elsevier Science 🌐 English ⚖ 594 KB

Th& paper aims to p&ce neural networks in the conte.\t ol'booh'an citz'ldt complexit.l: 1,1~, de/itte aplm~priate classes qlfeedybrward neural networks with specified fan-in, accm'ac)' olcomputation and depth and ttsing techniques" o./commzmication comph:¥ity proceed to show t/tat the classes.fit in