Development and applications of a sequential, minimal, radial basis function (RBF) neural network learning algorithm

Development and applications of a sequential, minimal, radial basis function (RBF) neural network learning algorithm

This thesis presents a new sequential learning algorithm for realizing a minimal Radial Basis Function (RBF) neural network, referred to as M-RAN (Minimal Resource Allocation Network). Unlike most of the classical RBF neural networks with the number of hidden neurons fixed apriori, the network st...

Full description

Saved in:
Bibliographic Details
Main Author: Lu, Ying Wei.
Other Authors: Narasimhan, Sundararajan
Format: Theses and Dissertations
Published: 2010
Subjects:
DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Online Access:http://hdl.handle.net/10356/39014
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Description
Summary:This thesis presents a new sequential learning algorithm for realizing a minimal Radial Basis Function (RBF) neural network, referred to as M-RAN (Minimal Resource Allocation Network). Unlike most of the classical RBF neural networks with the number of hidden neurons fixed apriori, the network structure is dynamic in the proposed M-RAN algorithm.

Similar Items