A hierarchial neural network implementation for forecasting

Author

Ozbayoglu, Murat A. ; Dagli, Cihan H. ; Fulkerson, Bill

Author_Institution

Dept. of Eng. Manage., Missouri Univ., Rolla, MO, USA

Volume

5

fYear

1994

fDate

27 Jun-2 Jul 1994

Firstpage

3184

Abstract

In this paper, a hierarchical neural network architecture for forecasting time series is presented. The architecture is composed of two hierarchical levels using a maximum likelihood competitive learning algorithm. The first level of the system has three experts each using backpropagation and a gating network to partition the input space in order to map the input vectors to the output vectors. The second level of the hierarchical network has an expert using fuzzy ART for producing the correct trend coming from the first level. The experiments show that the resulting network is capable of forecasting the changes in the input and identifying the trends correctly

Keywords

ART neural nets; backpropagation; forecasting theory; fuzzy neural nets; maximum likelihood estimation; multilayer perceptrons; time series; unsupervised learning; backpropagation; gating network; hierarchical neural network architecture; maximum likelihood competitive learning algorithm; time series forecasting; Aerospace industry; Artificial neural networks; Extrapolation; Neural networks; Partitioning algorithms; Power system modeling; Power system simulation; Predictive models; Random variables; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1901-X

Type

conf

DOI

10.1109/ICNN.1994.374744

Filename

374744