Optimal Artificial Neural Network using Particle Swarm Optimization

Abstract

Artificial neuron networks (ANNs) are widely used for data analyticS in broad areas of engineering applications and commercial services. The ANN has one to two hidden layers. In advanced ANN, multiple-layer ANN is used where the network extracts different features until it can recognize what it is looking for through deep learning approaches. Usually, a backpropagation algorithm is used to train the network and fix weights and biases associated with each network neuron. This paper proposes a particle swarm optimization (PSO) based algorithm for training ANN for better performance and accuracy. Two types of ANN models and their training using PSO have been developed. The performance of the developed models has been analyzed on a standard dataset. Also, the effectiveness and suitability of the developed approach have been demonstrated through statistics of the obtained results.

Keywords

• activation function
• hidden layer
• input layer
• output layer
• transfer function
• artificial neural networks
• particle swarm optimization