✦ LIBER ✦

Reformulated radial basis function neural networks with adjustable weighted norms

✍ Scribed by Mary M. Randolph-Gips; Nicolaos B. Karayiannis

Publisher: John Wiley and Sons
Year: 2003
Tongue: English
Weight: 201 KB
Volume: 18
Category: Article
ISSN: 0884-8173
DOI: 10.1002/int.10133

No coin nor oath required. For personal study only.

✦ Synopsis

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 norm weights that can be updated during learning to facilitate the implementation of the desired input-output mapping. Experiments involving classification and function approximation tasks verify that the proposed RBF neural networks outperform conventional RBF neural networks and reformulated RBF neural networks employing fixed Euclidean norms. Reformulated RBF neural networks with adjustable weighted norms are also strong competitors to conventional feedforward neural networks in terms of performance, implementation simplicity, and training speed.

📜 SIMILAR VOLUMES

Control of robots using radial basis fun

Control of robots using radial basis function neural networks with dead-zone

✍ Abraham K. Ishihara; Johan van Doornik; Shahar Ben-Menahem 📂 Article 📅 2011 🏛 John Wiley and Sons 🌐 English ⚖ 334 KB 👁 1 views

## Abstract In this paper, we examine the control of robot manipulators utilizing a Radial Basis Function (RBF) neural network. We are able to remove the typical requirement of Persistence of Excitation (PE) for the desired trajectory by introducing an __error minimizing dead‐zone__ in the learning

Hyper Radial Basis Function Neural Netwo

Hyper Radial Basis Function Neural Networks for Interference Cancellation with Nonlinear Processing of Reference Signal

✍ Sergiy A. Vorobyov; Andrzej Cichocki 📂 Article 📅 2001 🏛 Elsevier Science 🌐 English ⚖ 755 KB

Efficient interference cancellation often requires nonlinear processing of a reference signal. In this paper, hyper radial basis function (HRBF) neural networks for adaptive interference cancellation is developed. We show that the HRBF networks, with an appropriate learning algorithm, is able to app