𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Relation between generalized radial basis function (GRBF) networks and neural networks

✍ Scribed by Akio Miyazaki; Tsuyoshi Yamada


Book ID
112079618
Publisher
John Wiley and Sons
Year
1993
Tongue
English
Weight
587 KB
Volume
76
Category
Article
ISSN
1042-0967

No coin nor oath required. For personal study only.


📜 SIMILAR VOLUMES


Evolutionary -Gaussian radial basis func
✍ Francisco Fernández-Navarro; César Hervás-Martínez; P.A. Gutiérrez; M. Carbonero 📂 Article 📅 2011 🏛 Elsevier Science 🌐 English ⚖ 564 KB

This paper proposes a radial basis function neural network (RBFNN), called the q-Gaussian RBFNN, that reproduces different radial basis functions (RBFs) by means of a real parameter q. The architecture, weights and node topology are learnt through a hybrid algorithm (HA). In order to test the overal

Reformulated radial basis function neura
✍ Mary M. Randolph-Gips; Nicolaos B. Karayiannis 📂 Article 📅 2003 🏛 John Wiley and Sons 🌐 English ⚖ 201 KB

This article presents a new family of reformulated radial basis function (RBF) neural networks that employ adjustable weighted norms to measure the distance between the training vectors and the centers of the radial basis functions. The reformulated RBF model introduced in this article incorporates