Optimal number of neurons for a two layer neural network model of a process

Neural networks are known as powerful tools to represent the essential properties of nonlinear processes because of their global approximation property. However, a key problem in modeling nonlinear processes by neural networks is the determination of neuron numbers. In this paper, a data based strat...

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Bibliographic Details
Published inSICE Annual Conference 2011 pp. 2216 - 2221
Main Authors Asadi, M. S., Fatehi, A., Hosseini, M., Sedigh, A. K.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2011
Subjects
Approximation methods
bicoherence test
Biological neural networks
Complexity theory
Feedforward neural networks
Frequency domain analysis
Inphase-quadrature demodulation
neural network
Neurons
Nonlinearity measure
Training
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Summary:Neural networks are known as powerful tools to represent the essential properties of nonlinear processes because of their global approximation property. However, a key problem in modeling nonlinear processes by neural networks is the determination of neuron numbers. In this paper, a data based strategy for determining number of hidden layer neurons based on the Barrons work, describing function analysis and bicoherence nonlinearity measure is proposed. The proposed algorithm is evaluated for a pH neutralization process. It is shown that this algorithm has acceptable results.
ISBN:9781457707148
1457707144