NDLI: Approximation property of multi-layer neural network (MLNN) and its application in nonlinear simulation

Content Provider	IEEE Xplore Digital Library
Author	Zhang Wei Yu Yinglin Jia Qing
Copyright Year	1991
Description	Author affiliation: Inst. of Electron. Eng. & Autom., South China Univ. of Technol., Guangzhou, China (Zhang Wei; Yu Yinglin)
Abstract	Presents the approximation properties of multilayer neural networks (MLNN) and their application in nonlinear simulation and analysis. The authors give a direct proof of the approximation ability for a single input-output MLNN with hidden units with sigmoid activation functions, and also give the relationship between the best polynomial approximation and the number of MLNN hidden units. Based on the analysis, the authors propose an MLNN model with hybrid sigmoid-Gaussian activation functions. To verify the idea, they present experiments and results of nonlinear simulation and analysis by MLNN for a solid-state power amplifier. These results prove that the proposed method has general application in nonlinear engineering simulations.
Starting Page	171
Ending Page	176
File Size	261563
Page Count	6
File Format	PDF
ISBN	0780301641
DOI	10.1109/IJCNN.1991.155170
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1991-07-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Multi-layer neural network Intelligent networks Function approximation Analytical models Artificial neural networks Space technology Neural networks Polynomials Solid modeling Solid state circuits
Content Type	Text
Resource Type	Article

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