Hanan A. R. Akkar; Faris B. Ali Jasim

Intelligent Training Algorithm for Artificial Neural Network EEG Classifications

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Author(s)

Hanan A. R. Akkar ^1,* Faris B. Ali Jasim ¹

1. University Of Technology/ Electrical Engineering Department, Baghdad, Iraq

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2018.05.04

Received: 2 Aug. 2017 / Revised: 16 Oct. 2017 / Accepted: 20 Dec. 2017 / Published: 8 May 2018

Index Terms

Artificial neural network, Energy distribution, EEG, GRO, ABC, CSA, PSO, evolutionary algorithms

Abstract

Artificial neural networks (ANN) have been widely used in classification. They are complicated networks due to the training algorithm used to fix their weights. To achieve better neural network performance, many evolutionary and meta-heuristic algorithms are used to optimize the network weights. The aim of this paper is to implement recently evolutionary algorithms for optimizing neural weights such as Grass Root Optimization (GRO), Artificial Bee Colony (ABC), Cuckoo Search Optimization (CSA) and Practical Swarm Optimization (PSO). This ANN was examined to classify three classes of EEG signals healthy subjects, subjects with interictal epilepsy seizure, and subjects with ictal epilepsy seizures. The above training algorithms are compared according to classification rate, training and testing mean square error, average time, and maximum iteration.

Cite This Paper

Hanan A. R. Akkar, Faris B. Ali Jasim, "Intelligent Training Algorithm for Artificial Neural Network EEG Classifications", International Journal of Intelligent Systems and Applications(IJISA), Vol.10, No.5, pp.33-41, 2018. DOI:10.5815/ijisa.2018.05.04

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International Journal of Intelligent Systems and Applications (IJISA)