FDO-MLP

# FDO-MLP
This data is a matlab coding. It is an implementation of a reasearch work using Fitness Dependent Optimizer (FDO) algorithm for training a Multilayer Perceptron Neural Network (MLP), which is in the process of submitting to a journal.

Cite the following articles:

J. M. Abdullah and T. A. Rashid (2019). Fitness Dependent Optimizer: Inspired by the Bee Swarming Reproductive Process," in IEEE Access, vol. 7, pp. 43473-43486. DOI:https://doi.org/10.1109/ACCESS.2019.2907012

Rashid TA, Abbas DK, Turel YK (2019) A multi hidden recurrent neural network with a modified grey wolf optimizer. PLoS ONE 14(3): e0213237. https://doi.org/10.1371/journal.pone.0213237

Tarik A. Rashid and Nian Kh. Aziz (2016) Student Academic Performance Using Artificial Intelligence. ZANCO Journal of Pure and Applied Sciences, The official scientific journal of Salahaddin University-Erbil, ZJPAS, 28 (2); 56-69.https://doi.org/10.21271/zjpas.v28i2.544

Tarik A. Rashid (2015). Improvement on Classification Models of Multiple Classes through Effectual Processes. International Journal of Advanced Computer Science and Applications(IJACSA), 6(7). http://dx.doi.org/10.14569/IJACSA.2015.060709

S. Mirjalili, How effective is the GreyWolf optimizer in training multi-layer perceptrons, Applied Intelligence, In press, 2015, DOI: http://dx.doi.org/10.1007/s10489-014-0645-7

本数据集系Matlab编程实现，旨在利用适应性优化器（FDO）算法对多层感知器神经网络（MLP）进行训练，该研究工作正处于投稿期刊阶段。相关文献引用如下：

J. M. Abdullah 和 T. A. Rashid (2019)。适应性优化器：受蜜蜂群繁殖过程启发，《IEEE Access》，第7卷，第43473-43486页。DOI：https://doi.org/10.1109/ACCESS.2019.2907012

Rashid TA, Abbas DK, Turel YK (2019)。带有改进灰狼优化器的多层隐藏循环神经网络，《PLoS ONE》，第14卷（3）：e0213237。https://doi.org/10.1371/journal.pone.0213237

Tarik A. Rashid 和 Nian Kh. Aziz (2016)。利用人工智能评估学生学术表现，《扎诺科大学纯与应用科学杂志》（ZANCO Journal of Pure and Applied Sciences），萨拉赫丁大学-埃尔比勒官方科学期刊，第28卷（2），第56-69页。https://doi.org/10.21271/zjpas.v28i2.544

Tarik A. Rashid (2015)。通过有效过程改进多类别分类模型，《国际高级计算机科学及应用杂志》（International Journal of Advanced Computer Science and Applications, IJACSA），第6卷（7）。http://dx.doi.org/10.14569/IJACSA.2015.060709

S. Mirjalili。灰狼优化器在训练多层感知器中的有效性如何，《应用智能》（Applied Intelligence），待出版，2015，DOI：http://dx.doi.org/10.1007/s10489-014-0645-7