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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 8/2015

01.11.2015 | Original Article

Designing evolutionary feedforward neural networks using social spider optimization algorithm

verfasst von: Seyedeh Zahra Mirjalili, Shahrzad Saremi, Seyed Mohammad Mirjalili

Erschienen in: Neural Computing and Applications | Ausgabe 8/2015

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Abstract

Training feedforward neural networks (FNNs) is considered as a challenging task due to the nonlinear nature of this problem and the presence of large number of local solutions. The literature shows that heuristic optimization algorithms are able to tackle these problems much better than the mathematical and deterministic methods. In this paper, we propose a new trainer using the recently proposed heuristic algorithm called social spider optimization (SSO) algorithm. The trained FNN by SSO (FNNSSO) is benchmarked on five standard classification data sets: XOR, balloon, Iris, breast cancer, and heart. The results are verified by the comparison with five other well-known heuristics. The results prove that the proposed FNNSSO is able to provide very promising results compared with other algorithms.
Vorheriger Artikel Spiking neural P systems with structural plasticity
Nächster Artikel A bioinspired neural dynamics-based approach to tracking control of autonomous surface vehicles subject to unknown ocean currents

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Metadaten
Titel
Designing evolutionary feedforward neural networks using social spider optimization algorithm
verfasst von
Seyedeh Zahra Mirjalili
Shahrzad Saremi
Seyed Mohammad Mirjalili
Publikationsdatum
01.11.2015
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 8/2015
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-015-1847-6

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