Research on Brain Signals via Artificial Neural Network and Swarm Intelligence Algorithms

Year 2019, Volume: 7 Issue: 2, 27 - 37, 30.06.2019

Sema Yildirim Hasan Erdinç Koçer A.hakan Ekmekci

https://doi.org/10.18100/ijamec.475090

Abstract

Artificial
Neural Networks (ANNs) that are the ability to learn from theirs environment in
order to improve their performance are widely used in numerous applications. The
Backpropagation (BP) Algorithm is one of the most popular and effective model
of ANNs. However, since it uses gradient descent algorithm which attempts to
minimize the error of the network by moving gradient of the error curve, easily
get trapped at local minima. To avoid this problem, we proposed an ANNs and Swarm
Intelligence (SI) method, where Artificial Bee Colony (ABC) and Particle Swarm
Optimization (PSO) algorithms were operated for the Multilayer Perceptron
Neural Network (MLPNN) weights update. Two Electroencephalogram (EEG) datasets
were used to test the success of all algorithms including ABC-MLPNN, PSO-MLPNN and
conventional-MLPNN. Compared to conventional-MLPNN, higher success values were
obtained on each dataset with the proposed methods. Experimental results demonstrate that combined SI and MLPNN
algorithm has been increased the success of BP algorithm by avoiding local
minima. 

Keywords

Swarm intelligence, artificial neural networks, backpropagation, electroencephalogram, brain disorders, feature extraction/selection

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