Optimally pruned extreme learning machine with ensemble of regularization techniques and negative correlation penalty applied to automotive engine coldstart hydrocarbon emission identification
Investigating the applicability of soft computational techniques for identifying the coldstart effect in SI engines as a highly nonlinear and transient phenomenon. Proposing an ensemble framework to increase the robustness of regularization techniques. Taking the advantages of extreme learning machine to provide fast and efficient identification systems. Extending the applications of negative correlation based ensemble identifiers to a really important engineering problem.
extreme learning machines regularization coldstart negative correlation based ensembles automotive engine modeling