Neural network as a simulation metamodel in economic analysis of risky projects

✍ Scribed by Adedeji B. Badiru; David B. Sieger

• Publisher: Elsevier Science
• Year: 1998
• Tongue: English
• Weight: 863 KB
• Volume: 105
• Category: Article
• ISSN: 0377-2217
• DOI: 10.1016/s0377-2217(97)00029-5

✦ Synopsis

An artificial neural network (ANN) model for economic analysis of risky projects is presented in this paper. Outputs of conventional simulation models are used as neural network training inputs. The neural network model is then used to predict the potential returns from an investment project having stochastic parameters. The nondeterministic aspects of the project include the initial investment, the magnitude of the rate of return, mad the investment period. Backpropagation method is used in the neural network modeling. Sigmoid and hyperbolic tangent functions are used in the learning aspect of the system. Analysis of the outputs of the neural network model indicates that more predictive capability can be achieved by coupling conventional simulation with neural network approaches. The trained network was able to predict simulation output based on the input values with very good accuracy for conditions not in its training set. This allowed an analysis of the future performance of the investment project without having to run additional expensive and time-consuming simulation experiments.