Using Genetic Algorithm Based Variable Selection to Improve Neural Network Models for Real-World Systems

Real-world systems are often modeled by sampling sensor data taken during system operation. System states may not be all known or measurable, sensor data may be biased or noisy, and it is not often known which sensor data may be useful for predictive modeling. Neural network models generated from this data must therefore rely on how effectively the chosen sensor data represents the system. Genetic algorithms may help to address this problem by determining a near optimal subset of sensor variables most appropriate to produce good models. This paper describes the use of genetic algorithms to optimize variable selection to determine inputs into a neural network system model. The use of this technique for modeling a typical industrial application, a liquid fed ceramic melter, and the results of the genetic search to optimize the neural network model for this application, are described.