title: Reproducing chaos by variable structure recurrent neural networks.
abstract: In this paper, we present a new approach for chaos reproduction using variable structure recurrent neural networks (VSRNN). A neural network identifier is designed, with a variable structure that will change according to its output performance as compared to the given orbits of an unknown chaotic systems. A tradeoff between identification errors and computational complexity is discussed.
authors.affiliation: CINVESTAV, Unidad Guadalajara, Mexico.
authors.forename: Ramon A
authors.initials: RA
authors.lastname: Felix
authors.forename: Edgar N
authors.initials: EN
authors.lastname: Sanchez
authors.forename: Guanrong
authors.initials: G
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003661:Decision Support Techniques; D005246:Feedback; D016015:Logistic Models; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated / Q000379:methods*