AN Averkin - Statistics and Economics, 2016 - statecon.rea.ru
HYBRID MODULAR NEURAL NETWORKS | Averkin | Statistics and Economics Statistics and Economics Statistika i èkonomika eng | рус User ISSN 2500-3925 (Print) Preview User …
This chapter is devoted to numerous applications of fuzzy neural networks in economy and financial sphere. In the Sect. 4.2 the problem of forecasting macroeconomic indicators of …
This paper proposes a novel nonlinear ensemble forecasting model integrating functional link (FL) with radial basis function (RBF) neural network in order to improve prediction …
In this work, we present an approach for fuzzy aggregation of neural networks for forecasting. The interval type-3 aggregator is used to combine the outputs of the networks to …
L Falat, D Marcek, M Durisova - The Scientific World Journal, 2016 - Wiley Online Library
This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in …
YU Lean, S Wang, KK Lai - Journal of Computational Information …, 2005 - researchgate.net
In this study, a triple-phase support vector machine based neural network ensemble model is proposed for exchange rates forecasting. In the first phase, many different single neural …
Y Chen, L Peng, A Abraham - International symposium on neural networks, 2006 - Springer
Forecasting exchange rates is an important financial problem that is receiving increasing attention especially because of its difficulty and practical applications. This paper proposes a …
The dynamic nonlinearity approach, coupled with the exchange rate data series, makes its future predictions difficult. Sophisticated methods are highly desired for effective prediction …
D Marcek, J Babel, L Falat - … of Multiple-Valued Logic & Soft …, 2019 - search.ebscohost.com
In this paper, we implement an effective way for forecasting financial time series with nonlinear relationships. We use the artificial neural network of feedforward type for making …