In this paper fields of research are modular neural networks and consistent real-valued logic
as a technique for aggregation of the modules. Aggregation is done using logical and …

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 …

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 …

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 …

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 …

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 …