Abstract
This paper shows the use of particle swarm optimization (PSO) in the design of a neural network ensemble with type-1 and type-2 fuzzy integration of responses for time series prediction. The considered time series in this paper for testing the hybrid approach is the US/Dollar MX time series. Simulation results show that the hybrid ensemble approach, combining neural networks and fuzzy logic, produces good prediction of the dollar time series.
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Acknowledgments
We would like to express our gratitude to CONACYT, and Tijuana Institute of Technology for the facilities and resources granted for the development of this research.
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Pulido, M., Melin, P., Castillo, O. (2016). Design of Ensemble Neural Networks for Predicting the US Dollar/MX Time Series with Particle Swarm Optimization. In: Zadeh, L., Abbasov, A., Yager, R., Shahbazova, S., Reformat, M. (eds) Recent Developments and New Direction in Soft-Computing Foundations and Applications. Studies in Fuzziness and Soft Computing, vol 342. Springer, Cham. https://doi.org/10.1007/978-3-319-32229-2_23
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DOI: https://doi.org/10.1007/978-3-319-32229-2_23
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