A hybrid algorithm based on artificial bat and backpropagation algorithms for multiplicative neuron model artificial neural networks

Bas, E. and Egrioglu, E. and Yolcu, U. (2020) A hybrid algorithm based on artificial bat and backpropagation algorithms for multiplicative neuron model artificial neural networks. Journal of Ambient Intelligence and Humanized Computing. ISSN 1868-5137

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Abstract

In the literature, the multiplicative neuron model artificial neural networks are trained by gradient-based or some artificial intelligence optimization algorithms. It is well known that the hybrid algorithms give successful results than classical algorithms in the literature and the use of hybrid systems increase day by day. From this point of view, different from other studies contribute to multiplicative neuron model artificial neural networks, the properties of an artificial intelligence optimization technique, artificial bat algorithm, and a gradient-based algorithm, backpropagation learning algorithm, is used together firstly by using the proposed method in this study. Thus, both a derivative and a heuristic algorithm were used together firstly for multiplicative neuron model artificial neural networks. The proposed method is applied to three well-known different real-world time series data. The performance of the proposed method is both compared with gradient-based optimization algorithms, some artificial optimization algorithms used for the training of artificial neural networks and some popular analyze methods. The analysis results show that the proposed hybrid method has superior performance than other methods.

Item Type:
Journal Article
Journal or Publication Title:
Journal of Ambient Intelligence and Humanized Computing
Additional Information:
The final publication is available at Springer via http://dx.doi.org/10.1007/s12652-020-01950-y
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700
Subjects:
ID Code:
143967
Deposited By:
Deposited On:
26 May 2020 09:54
Refereed?:
Yes
Published?:
Published
Last Modified:
26 May 2020 09:54