Closed loop identification with Youla parameterization and neural nets

System identification has become the fundamental pillar of the industry of the 21 century, since allows the integration of advanced model based control strategies with complex process; for diverse reasons, real process are in closed loop due to difference reasons making virtually impossible obtain a model using open loop techniques, in this work an innovative strategy to identify plants in closed loop using an indirect method based on Youla Kucera parameterization and neural nets is presented, stating 4 identifications subproblems with numerous potential applications on real problems