Fault Tolerance for faults in Artificial Neural Networks: Robust Fault Tolerance for Multinode faults in RBF Neural Networks

V, Saritha

ISBN 10: 6200324034 / ISBN 13: 9786200324030

Published by LAP LAMBERT Academic Publishing, 2019

About this Item

Seller Inventory # 26387102022

Bibliographic Details

• Title: Fault Tolerance for faults in Artificial Neural Networks: Robust Fault Tolerance for Multinode faults in RBF Neural Networks
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2019
• Language: English
• ISBN 10: 6200324034
• ISBN 13: 9786200324030
• Binding: Soft cover
• Condition: New

About this title

Synopsis

This bookk addresses the fault tolerance of RBF networks where all hidden nodes have the same fault rate and their fault probabilities are independent. Assuming that there is a Gaussian distributed noise in the output data, we have derived an objective function for robustly training an RBF network based on the Kullback–Leibler divergence. We also find that for a fault-tolerance regularizer some eigenvalues of the regularization matrix should be negative. For the Tipping’s regularizer and the OLS regularizer, the regularization matrices are positive or semipositive definite. Hence, they cannot efficiently handle the multinode open fault.