Using a heuristic approach to derive a grey-box model through an artificial neural network knowledge extraction technique

Authors:
William A. Young, II;Gary R. Weckman
Affiliations:
Ohio University, Department of Industrial and Systems Engineering, Russ College of Engineering and Technology, Stocker Center, 45701-2979, Athens, OH, USA;Ohio University, Department of Industrial and Systems Engineering, Russ College of Engineering and Technology, Stocker Center, 45701-2979, Athens, OH, USA
Venue:
Neural Computing and Applications
Year:
2010

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Abstract

Artificial neural networks (ANNs) are primarily used in academia for their ability to model complex nonlinear systems. Though ANNs have been used to solve practical problems in industry, they are not typically used in nonacademic environments because they are not very well understood, complicated to implement, or have the reputation of being a “black-box” model. Few mathematical models exist that outperform ANNs. If a highly accurate model can be constructed, the knowledge should be used to understand and explain relationships in a system. Output surfaces can be analyzed in order to gain additional knowledge about a system being modeled. This paper presents a systematic approach to derive a “grey-box” model from the knowledge obtained from the ANN. A database for an automobile’s gas mileage performance is used as a case study for the proposed methodology. The results show a greater ability to generalize system behavior than other benchmarked methods.