title: Nonlinear system identification based on internal recurrent neural networks.
abstract: A novel approach for nonlinear complex system identification based on internal recurrent neural networks (IRNN) is proposed in this paper. The computational complexity of neural identification can be greatly reduced if the whole system is decomposed into several subsystems. This approach employs internal state estimation when no measurements coming from the sensors are available for the system states. A modified backpropagation algorithm is introduced in order to train the IRNN for nonlinear system identification. The performance of the proposed design approach is proven on a car simulator case study.
authors.affiliation: Faculty of Computer Science, "Dunarea de Jos" University of Galaţi, Str. Domneasca No.111, 800211, Romania. gpuscasu@ugal.ro
authors.forename: Gheorghe
authors.initials: G
authors.lastname: Puscasu
authors.forename: Bogdan
authors.initials: B
authors.lastname: Codres
authors.forename: Alexandru
authors.initials: A
authors.lastname: Stancu
authors.forename: Gabriel
authors.initials: G
authors.lastname: Murariu
mesh_terms: D003198:Computer Simulation; D008959:Models, Neurological; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics