Title: Prediction of exchange rate using improved particle swarm optimised radial basis function networks

Authors: Trilok Nath Pandey; Alok Kumar Jagadev; Satchidananda Dehuri; Sung-Bae Cho

Addresses: Department of Computer Science and Engineering, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India ' Department of Computer Science and Engineering, KIIT (Deemed to be University), Bhubaneswa, Odisha, India ' Department of Information and Communication Technology, Fakir Mohan University, Balasore, Odisha, India ' Department of Computer Science, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 120-749, South Korea

Abstract: In this paper, a radial basis function neural network (RBFN) model has been trained by canonical particle swarm optimisation (PSO) and improved particle swarm optimisation (IMPSO) algorithms to efficiently predict the exchange rate of Indian rupees against the exchange rate of G-7 countries for future days. We have used two variants of PSO such as canonical PSO and IMPSO for optimising the parameters of radial basis function neural network through learning from the past data of exchange rate prediction. Here, we have considered 43 countries' exchange rates to predict the Indian rupees against the G-7 countries. Forty-three exchange rates have been collected and based on their correlation analysis a dataset has been prepared to validate the proposed model. In addition, a fair comparison has been carried out between IMPSO tuned RBFN and canonical PSO tuned RBFN with respect to the results obtained by varying the number of iterations for future days' prediction. From the experimental results, it is observed that the predictive performance of IMPSO tuned RBFN modelling the case of higher number of iterations is promising vis-à-vis canonical PSO tuned RBFNs model.

Keywords: radial basis function network; RBFN; neural network; radial basis function; canonical particle swarm optimisation; improved particle swarm optimisation model; exchange rate.

DOI: 10.1504/IJAIP.2022.126695

International Journal of Advanced Intelligence Paradigms, 2022 Vol.23 No.3/4, pp.332 - 356

Received: 27 Jun 2017
Accepted: 10 Mar 2018

Published online: 03 Nov 2022 *

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