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Empirical Study of FFANN Tolerance to Weight Stuck at Max/Min Fault

ISSN : 0975-900X
Journal from gdlhub / 2017-08-14 11:52:33
Oleh : Amit Prakash Singh , Chandra Shekhar Rai and Pravin Chandra, International Journal of Artificial Intelligence & Applications
Dibuat : 2012-06-25, dengan 1 file

Keyword : Artificial Neural Network, Fault Tolerance, Weight Fault
Subjek : Empirical Study of FFANN Tolerance to Weight Stuck at Max/Min Fault
Url : http://airccse.org/journal/ijaia/papers/0410ijaia2.pdf
Sumber pengambilan dokumen : Internet

Fault tolerance property of artificial neural networks has been investigated




with reference to the hardware model of artificial neural networks. Weight




fault is an important link, which causes breakup between two nodes. In this




paper three types of weight faults have been explained. Experiments have been




performed to demonstrate fault tolerance behavior of feedforward artificial




neural network for weight-stuck-MAX/MIN fault. Effect of weight-stuck-




MAX/MIN fault on trained network has been analyzed in this paper. The




obtained results suggest that networks are not fault tolerant to this type of




fault.

Deskripsi Alternatif :

Fault tolerance property of artificial neural networks has been investigated




with reference to the hardware model of artificial neural networks. Weight




fault is an important link, which causes breakup between two nodes. In this




paper three types of weight faults have been explained. Experiments have been




performed to demonstrate fault tolerance behavior of feedforward artificial




neural network for weight-stuck-MAX/MIN fault. Effect of weight-stuck-




MAX/MIN fault on trained network has been analyzed in this paper. The




obtained results suggest that networks are not fault tolerant to this type of




fault.

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