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ŠTENCL, M. ŠŤASTNÝ, J.
Original Title
Neural network learning algorithms comparison on numerical prediction of real data
Type
conference paper
Language
English
Original Abstract
In this paper we concentrate on prediction of future values based on the past course of a variable, Traditionally this task is solved using statistical analysis - first a time-series model is constructed and then statistical prediction algorithms are applied to it in order to obtain future values. This paper describes two learning algorithms for training Multi-layer perceptron networks, widely known Back propagation learning algorithm and Levenberg- Marquardt algorithm. Both of these methods are applied to solve prediction of real numerical time series represented by Czech household consumption expenditures. Tested dataset includes twenty-eight observations between the years 2001 and 2007. The observations are represented by quarterly data and the goal is to predict three future values for first three quarters of 2008. Predicted values of both experiments are compared with measured values. In the next step, a comparison of neural network topology efficiency regarding to learning algorithms is made.
Keywords
Back Propagation, Levenberg-Marquardt, Prediction of Time Series, Neural Networks
Authors
ŠTENCL, M.; ŠŤASTNÝ, J.
RIV year
2010
Released
23. 6. 2010
ISBN
978-80-214-4120-0
Book
Mendel 2010
Edition number
16
Pages from
280
Pages to
285
Pages count
6
BibTex
@inproceedings{BUT34565, author="Michael {Štencl} and Jiří {Šťastný}", title="Neural network learning algorithms comparison on numerical prediction of real data", booktitle="Mendel 2010", year="2010", number="16", pages="280--285", isbn="978-80-214-4120-0" }