Neural network modelling has become a great interest for many statisticians to be utilized in
various types of data as classification, regression, and time series. It also has been applied …

Various gradient based optimization techniques have been developed for optimizing neural
network weights. However, the stability of results in obtaining the optimal weights has …

Neural network modeling has become a special interest for many engineers and scientists to
be utilized in different types of data as time series, regression, and classification and have …

This research discusses the use of a class of heuristic optimization to obtain the weights in
neural network model for time series prediction. In this case, Feed Forward Neural Network …

One of the oldest known predictive analytics techniques is time series prediction. The target
in time series prediction is use historical data about a specific quantity to predicts value of …

This paper proposes the Particle Swarm Optimization model for enhancing the performance
of an Artificial Neural Network. The learning process of Artificial Neural Network requires a …

In this article, we combine convolutional neural networks and particle swarm optimization
techniques to design Recurrent Neural Network (RNN) architectures. The proposed particle …

Backpropagation (BP) algorithm is widely used to solve many real world problems by using
the concept of Multilayer Perceptron (MLP). However, major disadvantages of BP are its …

The performances of the statistical methods of time series forecast can be improved by
precise selection of their parameters. Various techniques are being applied to improve the …

In this paper, a new and simplified hybrid algorithm mixing the simplex method of Nelder
and Mead (NM) and particle swarm optimization algorithm (PSO), abbreviated as SNM-PSO …