DocumentCode :
443989
Title :
Approximations and adaptability of neural networks
Author :
Shi, Karen ; Fei, Shih-Huang ; Lin, Christina
Author_Institution :
Harvey Mudd Coll., Claremont, CA, USA
Volume :
1
fYear :
2005
fDate :
25-27 July 2005
Firstpage :
253
Abstract :
Given a neural network that approximates a given function, which describes some physical phenomenon. If the physical constraints and hence the given function change, neural networks must adapt to the physical world by changing their architecture. The new architecture may have more neurons, so the adaptable neural networks need this learning abilities that are not in traditional design.
Keywords :
learning (artificial intelligence); neural nets; learning ability; neural network adaptability; neural network approximations; Acceleration; Educational institutions; Equations; Input variables; Mathematical model; Neural network hardware; Neural networks; Neurons; USA Councils; Vectors; Adaptability; Approximations; Learning; Neural network;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Granular Computing, 2005 IEEE International Conference on
Print_ISBN :
0-7803-9017-2
Type :
conf
DOI :
10.1109/GRC.2005.1547278
Filename :
1547278
Link To Document :
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