Document Type

Thesis

Degree

Master of Science (MS)

Department

Please see currently inactive department below.

Department

Industrial and Systems Engineering

First Advisor's Name

Martha A. Centeno

First Advisor's Committee Title

Committee Chair

Second Advisor's Name

Armando Barreto

Third Advisor's Name

Kia Makki

Date of Defense

3-29-2004

Abstract

An integration framework for Neural Networks (NN) and Goal Driven Simulation (GDS) has been designed. It offers no constraints regarding number of variables (n>3) and it does not have domain restrictions. The effectiveness of the framework was tested by observing the computational time required for obtaining responses and for training, and by assessing its accuracy for different scenarios. This framework has achieved the automation objective set by GDS under a shorter time frame, as it reduces the time from more than 42 hours to less than 14. A trained NN generates responses to queries almost instantaneously. However, it requires time re-building and re-training new NNs when changes are made to the system represented by the model. If these changes are rare, the payoff is worthy as this approach gives users more flexibility.

Identifier

FI14060853

Comments

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