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Extreme Learning With Metaheuristic Optimization for Exchange Rate Forecasting

Extreme Learning With Metaheuristic Optimization for Exchange Rate Forecasting

Kishore Kumar Sahu, Sarat Chandra Nayak, Himansu Sekhar Behera
Copyright: © 2022 |Volume: 13 |Issue: 1 |Pages: 25
ISSN: 1947-9263|EISSN: 1947-9271|EISBN13: 9781683181514|DOI: 10.4018/IJSIR.295099
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MLA

Sahu, Kishore Kumar, et al. "Extreme Learning With Metaheuristic Optimization for Exchange Rate Forecasting." IJSIR vol.13, no.1 2022: pp.1-25. http://doi.org/10.4018/IJSIR.295099

APA

Sahu, K. K., Nayak, S. C., & Behera, H. S. (2022). Extreme Learning With Metaheuristic Optimization for Exchange Rate Forecasting. International Journal of Swarm Intelligence Research (IJSIR), 13(1), 1-25. http://doi.org/10.4018/IJSIR.295099

Chicago

Sahu, Kishore Kumar, Sarat Chandra Nayak, and Himansu Sekhar Behera. "Extreme Learning With Metaheuristic Optimization for Exchange Rate Forecasting," International Journal of Swarm Intelligence Research (IJSIR) 13, no.1: 1-25. http://doi.org/10.4018/IJSIR.295099

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Abstract

Model with better learning ability and lower structural complexity is desirous for accurate exchange rate forecasting. Faster convergence to optimal solutions has always been a goal for the researcher in building forecasting models. And this is achieved by extreme learning machines (ELMs) due to their single hidden layer architecture and superior generalization ability. ELM is a simple training algorithm used to find the hidden-output layer weights by a random selection of input-hidden layer weights. Metaheuristics algorithms like Fireworks algorithm (FWA), Chemical reaction optimization (CRO), and Teaching learning-based optimization (TLBO) are employed to pre-train the ELM owing to their fewer optimizing parameters. This article aims to pre-train ELM using the said metaheuristics separately, ensuring the optimal solution of a single feedforward network (SLFN) with improved accuracy. The pre-trained ELMs provide accurate results. The same was verified using other primitive optimization algorithms

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