Artificial neural networks (ANNs) have achieved great success in performing machine
learning tasks, including classification, regression, prediction, image processing, image …

Artificial neural network (ANN) training is one of the major challenges in using a prediction
model based on ANN. Gradient based algorithms are the most frequent training algorithms …

This paper presents a modified particle swarm optimization (PSO) with simulated annealing
(SA) technique. An improved PSO-based artificial neural network (ANN) is developed. The …

Presenting a satisfactory and efficient training algorithm for artificial neural networks (ANN)
has been a challenging task in the supervised learning area. Particle swarm optimization …

In this paper, a novel particle swarm optimization model for radial basis function neural
networks (RBFNN) using hybrid algorithms to solve classification problems is proposed. In …

Traditional particle swarm optimization (PSO) has good global search ability, but it easily
loses its diversity and thus leads to premature convergence. Gradient descent methods such …

Pattern classification is one of the popular applications of neural networks. However, training
the neural networks is the most essential phase. Traditional training algorithms (eg Back …

Artificial Neural Networks (ANNs) are becoming increasingly useful in numerous areas as
they have a myriad of applications. Prior to using ANNs, the network structure needs to be …

Artificial Neural Network (ANN) design is a complex task because its performance depends
on the architecture, the selected transfer function, and the learning algorithm used to train …

Training optimization plays a vital role in the development of convolution neural network
(CNN). CNNs are hard to train because of the presence of multiple local minima. The …