Multilayer Perceptrons Which Are Tolerant to Multiple Faults and Learnings to Realize Them
Authors:
- 1 January 2014
- book chapter
- Published by Springer Nature
- p. 42-63
Abstract
No abstract availableKeywords
This publication has 12 references indexed in Scilit:
- On-Line Node Fault Injection Training Algorithm for MLP Networks: Objective Function and Convergence AnalysisIEEE Transactions on Neural Networks and Learning Systems, 2011
- Objective Functions of Online Weight Noise Injection Training Algorithms for MLPsIEEE Transactions on Neural Networks, 2010
- Convergence and Objective Functions of Some Fault/Noise-Injection-Based Online Learning Algorithms for RBF NetworksIEEE Transactions on Neural Networks, 2010
- On Objective Function, Regularizer, and Prediction Error of a Learning Algorithm for Dealing With Multiplicative Weight NoiseIEEE Transactions on Neural Networks, 2008
- Learning Algorithms Which Make Multilayer Neural Networks Multiple-Weight-and-Neuron-Fault TolerantIEICE Transactions on Information and Systems, 2008
- On fault injection approaches for fault tolerance of feedforward neural networks2002
- Evaluation function for fault tolerant multi-layer neural networks2000
- A novel learning algorithm which improves the partial fault tolerance of multilayer neural networksNeural Networks, 1999
- Complete and partial fault tolerance of feedforward neural netsIEEE Transactions on Neural Networks, 1995
- Enhanced MLP performance and fault tolerance resulting from synaptic weight noise during trainingIEEE Transactions on Neural Networks, 1994