Neurocomputing
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Approximation capabilities of multilayer feedforward networks
Neural Networks
An investigation of the use of feedforward neural networks for forecasting
An investigation of the use of feedforward neural networks for forecasting
IEEE Transactions on Neural Networks
Dynamic learning rate optimization of the backpropagation algorithm
IEEE Transactions on Neural Networks
Neural modeling for time series: A statistical stepwise method for weight elimination
IEEE Transactions on Neural Networks
AI techniques in modelling, assignment, problem solving and optimization
Engineering Applications of Artificial Intelligence
A dynamic artificial neural network model for forecasting nonlinear processes
Computers and Industrial Engineering
An artificial neural network (p,d,q) model for timeseries forecasting
Expert Systems with Applications: An International Journal
Measuring effectiveness of a dynamic artificial neural network algorithm for classification problems
Expert Systems with Applications: An International Journal
Using artificial neural network models in stock market index prediction
Expert Systems with Applications: An International Journal
A new class of hybrid models for time series forecasting
Expert Systems with Applications: An International Journal
Application of type-2 neuro-fuzzy modeling in stock price prediction
Applied Soft Computing
Automated text classification using a dynamic artificial neural network model
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
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Artificial neural networks (ANN), have shown to be an effective, general-purpose approach for pattern recognition, classification, clustering, and prediction. Traditional research in this area uses a network with a sequential iterative learning process based on the feed-forward, back-propagation algorithm. In this paper, we introduce a model that uses a different architecture compared to the traditional neural network, to capture and forecast nonlinear processes. This approach utilizes the entire observed data set simultaneously and collectively to estimate the parameters of the model. To assess the effectiveness of this method, we have applied it to a marketing data set and a standard benchmark from ANN literature (Wolf's sunspot activity data set). The results show that this approach performs well when compared with traditional models and established research.