DocumentCode :
3442336
Title :
Sensitivity analysis for minimization of input data dimension for feedforward neural network
Author :
Zurada, Jacek M. ; Malinowski, Aleksander ; Cloete, Ian
Author_Institution :
Louisville Univ., KY, USA
Volume :
6
fYear :
1994
fDate :
30 May-2 Jun 1994
Firstpage :
447
Abstract :
Multilayer feedforward networks are often used for modeling complex relationships between the data sets. Deleting unimportant data components in the training sets could lead to smaller networks and reduced-size data vectors. This can be achieved by analyzing the total disturbance of network outputs due to perturbed inputs. The search for redundant data components is performed for networks with continuous outputs and is based on the concept in sensitivity of linearized neural networks. The formalized criteria and algorithm for pruning data vectors are formulated and illustrated with examples
Keywords :
feedforward neural nets; minimisation; sensitivity analysis; feedforward neural network; input data dimension; linearized neural networks; minimization; multilayer feedforward networks; redundant data components; sensitivity analysis; Africa; Analytical models; Backpropagation; Electronic mail; Feedforward neural networks; Multi-layer neural network; Neural networks; Neurons; Redundancy; Sensitivity analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Circuits and Systems, 1994. ISCAS '94., 1994 IEEE International Symposium on
Conference_Location :
London
Print_ISBN :
0-7803-1915-X
Type :
conf
DOI :
10.1109/ISCAS.1994.409622
Filename :
409622
Link To Document :
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