The uncertainty of neural model influences the effectiveness of the neural model-based FDI and FTC systems. The application of the GMDH approach to the state-space neural model structure selection allows reducing the model uncertainty. The state-space representation of the neural model enables to develop a new technique of estimation of the neural model inputs based on the RUIF. This result enables performing robust fault detection and isolation of the actuators.