The influence of parameter initialization on the training time and accuracy of a nonlinear regression model

In order to construct a nonlinear regression model we have to accurately (in some sense) initialize parameters of the model. In this work we performed comparison of several widely used methods and several newly developed approached for initialization of parameters of a regression model, represented...

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Veröffentlicht in:	Journal of communications technology & electronics Jg. 61; H. 6; S. 646 - 660
Hauptverfasser:	Burnaev, E. V., Erofeev, P. D.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Moscow Pleiades Publishing 01.06.2016 Springer Springer Nature B.V
Schlagworte:	Accuracy Algorithms Approximation Artificial neural networks Communications Engineering Communications equipment Communications technology Comparative analysis Decomposition Dependence Dictionaries Engineering Experiments Learning Mathematical analysis Mathematical models Mathematical Models and Computational Methods Mean square errors Networks Neural networks Nonlinear systems Nonlinearity Regression Regression analysis Studies Support vector machines parameter initialization nonlinear regression approximation neural networks RProp algorithm SCAWI algorithm error back propagation algorithm
ISSN:	1064-2269, 1555-6557
Online-Zugang:	Volltext
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