DocumentCode
507990
Title
The Modeling Ability and Its Effectivity for Multi-layer ADALINE NN
Author
Su, Yingying ; Li, Taifu ; Wang, Debiao ; Liu, Yucheng ; Qi, Danping
Author_Institution
Coll. of Electron. Inf. Eng., Chongqing Univ. of Sci. & Technol., Chongqing, China
Volume
2
fYear
2009
fDate
14-16 Aug. 2009
Firstpage
449
Lastpage
453
Abstract
Aimed at the phenomenon that there is few theories and applications about multi-layer ADALINE neural network (NN), in this paper, mathematic expressions of the multi-input/ single-output (MISO) and multi-input/ multi-output (MIMO) ADALINE NN with multi-layer had been deduced according to the theory of linear neuron transfer function. At result, expressions are also linear functions respect with input variables with more complex weights and thresholds. In order to verify the equivalence of multi-layer ADALINE NNand single-layer ADALINE NN, two examples had been simulated with MATLAB software for function approximation, respectively. The simulation results showed that the multi-layer ADALINE NN can approximate linear functions but not as well as the single-layer ADALINE NN does. In conclusion, the existence of multi-layer adaline NN is not necessary, and its function can be simply realized with the single-layer ADALINE NN completely.
Keywords
function approximation; neural nets; transfer functions; approximate linear function; function approximation; linear functions; linear neuron transfer function; modeling ability; multiinput multioutput ADALINE NN; multiinput single-output ADALINE NN; multilayer ADALINE NN; multilayer ADALINE neural network; Function approximation; Input variables; MATLAB; MIMO; Mathematical model; Mathematics; Multi-layer neural network; Neural networks; Neurons; Transfer functions; ADALINE neural network; Equivalence; Multi-layer; Single-layer;
fLanguage
English
Publisher
ieee
Conference_Titel
Natural Computation, 2009. ICNC '09. Fifth International Conference on
Conference_Location
Tianjin
Print_ISBN
978-0-7695-3736-8
Type
conf
DOI
10.1109/ICNC.2009.700
Filename
5364496
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