PARTICLE SWARM OPTIMIZATION (PSO) FOR TRAINING OPTIMIZATION ON CONVOLUTIONAL NEURAL NETWORK (CNN)
Abstract: Neural network
attracts plenty of researchers lately. Substantial number of renowned
universities have developed neural network for various both academically and
industrially applications. Neural network shows considerable performance on
various purposes. Nevertheless, for complex applications, neural network’s
accuracy significantly deteriorates. To tackle the aforementioned drawback, lot
of researches had been undertaken on the improvement of the standard neural
network. One of the most promising modifications on standard neural network for
complex applications is deep learning method. In this paper, we proposed the
utilization of Particle Swarm Optimization (PSO) in Convolutional Neural
Networks (CNNs), which is one of the basic methods in deep learning. The use of
PSO on the training process aims to optimize the results of the solution
vectors on CNN in order to improve the recognition accuracy. The data used in
this research is handwritten digit from MNIST. The experiments exhibited that
the accuracy can be attained in 4 epoch is 95.08%. This result was better than
the conventional CNN and DBN. The execution
time was also almost similar to the conventional CNN. Therefore, the proposed
method was a promising method.
Keywords: deep learning,
convolutional neural network, particle swarm optimization, deep belief network
Author: Arie Rachmad
Syulistyo, Dwi Marhaendro Jati Purnomo, Muhammad Febrian Rachmadi, Adi Wibowo
Journal Code: jptkomputergg160006
