A dynamic artificial neural network model for forecasting nonlinear processes

Authors:
M. Ghiassi;Stanley Nangoy
Affiliations:
Operations & Management Information Systems, Santa Clara University, 500 El Camino Real, Santa Clara, CA 95053-0388, United States;Protiviti, Inc., 2880 Lakeside Dr., Suite 300, Santa Clara, CA 95054-2826, United States
Venue:
Computers and Industrial Engineering
Year:
2009

Citing 3
Cited 1

Original Contribution: Forecasting the behavior of multivariate time series using neural networks

Neural Networks
A dynamic architecture for artificial neural networks

Neurocomputing
Neural-network construction and selection in nonlinear modeling

IEEE Transactions on Neural Networks

Predictive models with endogenous variables for quality control in customized scenarios affected by multiple setups

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Abstract

This paper presents the development of a dynamic architecture for artificial neural network (DAN2) model for solving nonlinear forecasting and pattern recognition problems. DAN2 is a data driven, feed forward, multilayer, dynamic architecture that is based on the principle of learning and accumulating knowledge at each layer and propagating and adjusting this knowledge forward to the next layer. Model building is automatically and dynamically repeated until a model that accurately captures the behavior of the process is determined. The resulting model is then used to forecast future values. To assess DAN2's effectiveness, we present forecasting results for a variety of nonlinear processes that have been extensively studied in the literature and report comparative results. The set of nonlinear processes considered covers most nonlinear formulations facing researchers. We show DAN2 to be more accurate and to perform consistently better than alternative approaches employed in forecasting nonlinear processes.