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Erschienen in:
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2017 | OriginalPaper | Buchkapitel

Improving the Predictability of GRNN Using Fruit Fly Optimization and PCA: The Nile Flood Forecasting

verfasst von : Mohammed E. El-Telbany

Erschienen in: Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016

Verlag: Springer International Publishing

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Abstract

Generalized regression neural network (GRNN) is commonly used for function approximation. The predictability of GRNN for Nile flood forecasting is investigated through optimization of the smoothing parameter using fruit-fly (FOA) algorithm. Due to the excess number of inputs that are highly correlated, principal component analysis (PCA) is used to reduce the dimension of a set of linearly correlated variables to improve forecasting task. Our empirical experiment shows that the performance of GRNN is better than other neural network included in this paper. The results of the prediction made by the proposed model showed a close fit with the actual data.

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Vorheriges Kapitel An Energy Management Approach in Hybrid Energy System Based on Agent’s Coordination
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Metadaten
Titel
Improving the Predictability of GRNN Using Fruit Fly Optimization and PCA: The Nile Flood Forecasting
verfasst von
Mohammed E. El-Telbany
Copyright-Jahr
2017
Buch
Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016

Print ISBN: 978-3-319-48307-8

Electronic ISBN: 978-3-319-48308-5

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-48308-5_30