Please use this identifier to cite or link to this item:
Title: Performance evaluation of radial basis function neural networks
Authors: Arun Kumar.
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Issue Date: 1999
Abstract: The work was done to compare the performance of the radial basis function neural networks with that of back propagation neural networks. The comparison was made both in the field of function approximation and pattern recognition. Cosine function and the hermite's polynomial were used for function approximation comparison. For pattern recognition problem, a set of twenty-six English alphabets and another set of ten numeric digits were used. The various comparison parameters taken into account included training time for the particular network and the average absolute output error in case of noisy input. For the case of the noisy input data, results for various levels of noise were studied.
Schools: School of Electrical and Electronic Engineering 
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

Files in This Item:
File Description SizeFormat 
  Restricted Access
7.65 MBAdobe PDFView/Open

Page view(s)

Updated on Sep 25, 2023


Updated on Sep 25, 2023

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.