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

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 (...

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Veröffentlicht in:	Cognitive computation 2014-09, Vol.6 (3), p.293-303
Hauptverfasser:	Feng, Ruibin, Xiao, Yi, Leung, Chi Sing, Tsang, Peter W. M., Sum, John
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Artificial Intelligence Artificial neural networks Biomedical and Life Sciences Biomedicine Brain Computation by Abstract Devices Computational Biology/Bioinformatics Errors Fault tolerance Machine learning Neural networks Neurosciences Radial basis function Random variables
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