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An adaptive learning rate backpropagation-type neural network for solvingn×nsystems on nonlinear algebraic equations
- Source :
- Mathematical Methods in the Applied Sciences. 39:2602-2616
- Publication Year :
- 2015
- Publisher :
- Wiley, 2015.
-
Abstract
- This paper presents an MLP-type neural network with some fixed connections and a backpropagation-type training algorithm that identifies the full set of solutions of a complete system of nonlinear algebraic equations with n equations and n unknowns. The proposed structure is based on a backpropagation-type algorithm with bias units in output neurons layer. Its novelty and innovation with respect to similar structures is the use of the hyperbolic tangent output function associated with an interesting feature, the use of adaptive learning rate for the neurons of the second hidden layer, a feature that adds a high degree of flexibility and parameter tuning during the network training stage. The paper presents the theoretical aspects for this approach as well as a set of experimental results that justify the necessity of such an architecture and evaluate its performance. Copyright © 2015 John Wiley & Sons, Ltd.
- Subjects :
- Mathematical optimization
Artificial neural network
General Mathematics
010102 general mathematics
Hyperbolic function
General Engineering
02 engineering and technology
Function (mathematics)
01 natural sciences
Backpropagation
Set (abstract data type)
Algebraic equation
Nonlinear system
0202 electrical engineering, electronic engineering, information engineering
Feature (machine learning)
020201 artificial intelligence & image processing
0101 mathematics
Algorithm
Mathematics
Subjects
Details
- ISSN :
- 01704214
- Volume :
- 39
- Database :
- OpenAIRE
- Journal :
- Mathematical Methods in the Applied Sciences
- Accession number :
- edsair.doi...........60f68d454513740869c8cbd1b7137338