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

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.