Performance of models based on a linear regression and neural networks | Zendy

Pero Radonja | Zendy

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Performance of models based on a linear regression and neural networks

Author(s) -

Pero Radonja

Publication year - 2013

Publication title -

serbian journal of electrical engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.133

H-Index - 5

eISSN - 2217-7183

pISSN - 1451-4869

DOI - 10.2298/sjee130606014r

Subject(s) - artificial neural network , linear regression , proper linear model , generalized linear model , simple linear regression , computer science , simple (philosophy) , linear model , regression , regression analysis , polynomial regression , artificial intelligence , mathematics , machine learning , algorithm , statistics , philosophy , epistemology

In this paper the comparison of models based on a linear regression and neural networks is presented. The analyzed models are the generalized profile function models, GPFM. The GPFM provides approximations of the individual models (individual stem profile models) of the objects using only two basic measurements. The performances of the obtained GPFM, by using the linear regression relations and neural networks are compared by a test platform in MATLwith a simple graphic user interface. It is shown that application of both linear regression and neural networks provides the efficient and robust generalized model with very good performances. [Projekat Ministarstva nauke Republike Srbije, br. EE-273015.B

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