A gain perturbation method to improve the generalization performance for the recurrent neural network misfire detector

A common constraint on the application of neural networks to diagnostics and control of mass manufactured systems is that training sets can only be obtained from limited number of system exemplars. As a consequence the variations of dynamic response in the systems pose a problem in obtaining excellent performance for the trained neural networks. In this paper we describe a gain perturbation method (GPM) to improve the generalization performance in neural network diagnostic monitors trained on a data set obtained from one individual vehicle and rested on data from the another vehicle. The results show significant improvement in the generalization performance for neural networks trained with GPM over the ones trained without GPM.