NDLI: An Improved Fault-Tolerant Objective Function and Learning Algorithm for Training the Radial Basis Function Neural Network

Content Provider	Springer Nature Link
Author	Feng, Ruibin Xiao, Yi Leung, Chi Sing Tsang, Peter W. M. Sum, John
Copyright Year	2013
Abstract	As the concept of artificial neural networks is based on the mechanism of the human brain, it is essential that a trained artificial neural network should exhibit certain amount of fault-tolerant ability. In this paper, we propose a fault-tolerant learning method for training radial basis function (RBF) networks that may contain the coexistence of the stuck-at-zero node fault and the stuck-at-one node fault. First, we provide a formulation for evaluating the mean square error of the faulty RBF networks. Next an objective function, together with an algorithm for training the fault-tolerant RBF networks, is developed. Subsequently, we derive a mean prediction error (MPE) formula to estimate the test set error of the faulty RBF networks. With the MPE formula, we can estimate the RBF width that leads to near-optimal fault-tolerant capability. Finally, simulations are conducted to demonstrate the feasibility of our method, as well as its compliance with the theoretical outcome.
Starting Page	293
Ending Page	303
Page Count	11
File Format	PDF
ISSN	18669956
Journal	Cognitive Computation
Volume Number	6
Issue Number	3
e-ISSN	18669964
Language	English
Publisher	Springer US
Publisher Date	2013-11-28
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Fault tolerance Generalization ability Radial basis function Regularization Neurosciences Computation by Abstract Devices Artificial Intelligence (incl. Robotics) Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Cognitive Neuroscience Computer Science Applications Computer Vision and Pattern Recognition

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