Please use this identifier to cite or link to this item: https://idr.l3.nitk.ac.in/jspui/handle/123456789/10543
Title: Development and performance evaluation of a novel knowledge guided artificial neural network (KGANN) model for exchange rate prediction
Authors: Jena, P.R.
Majhi, R.
Majhi, B.
Issue Date: 2015
Citation: Journal of King Saud University - Computer and Information Sciences, 2015, Vol.27, 4, pp.450-457
Abstract: This paper presents a new adaptive forecasting model using a knowledge guided artificial neural network (KGANN) structure for efficient prediction of exchange rate. The new structure has two parallel systems. The first system is a least mean square (LMS) trained adaptive linear combiner, whereas the second system employs an adaptive FLANN model to supplement the knowledge base with an objective to improve its performance value. The output of a trained LMS model is added to an adaptive FLANN model to provide a more accurate exchange rate compared to that predicted by either a simple LMS or a FLANN model. This finding has been demonstrated through an exhausting computer simulation study and using real life data. Thus the proposed KGANN is an efficient forecasting model for exchange rate prediction. 2015 The Authors.
URI: http://idr.nitk.ac.in/jspui/handle/123456789/10543
Appears in Collections:1. Journal Articles

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