Analysis & survey on fault tolerance in radial basis function networks

Author

Martolia, Richa ; Jain, Amit ; Singla, Laxya

Author_Institution

MCA Dept., Nat. Inst. Of Technol., Kurukshetra, India

fYear

2015

fDate

15-16 May 2015

Firstpage

469

Lastpage

473

Abstract

Conventional learning theory´s failure in training Neural Network to provide acceptable levels of generalization on the occurrences of fault in network has lead to the advent of Fault Tolerant Learning. Radial Basis Function networks are assumed to have in built Fault Tolerance capabilities. With this paper our attempt is to bring forth a detailed and time ordered survey of the literature available on Fault Tolerance in RBF networks. Methods, algorithms, measures for dealing with faults in RBF networks will be reported and analyzed. Future work along with directions is also presented.

Keywords

fault tolerant computing; learning (artificial intelligence); radial basis function networks; RBF networks; fault tolerant learning; neural network training; radial basis function networks; Artificial neural networks; Fault tolerance; Fault tolerant systems; Linear programming; Noise; Radial basis function networks; Training; Artificial Neural Network; Fault Tolerant Learning; Node Faults; Radial Basis Function Networks; Regularization theory; Weight Faults;

fLanguage

English

Publisher

ieee

Conference_Titel

Computing, Communication & Automation (ICCCA), 2015 International Conference on

Conference_Location

Noida

Print_ISBN

978-1-4799-8889-1

Type

conf

DOI

10.1109/CCAA.2015.7148422

Filename

7148422