Neural Network Systems Techniques and Applications

Cornelius T. Leondes received his B.S., M.S., and Ph.D. from the University of Pennsylvania and has held numerous positions in industrial and academic institutions. He is currently a Professor Emeritus at the University of California, Los Angeles. He has also served as the Boeing Professor at the University of Washington and as an adjunct professor at the University of California, San Diego. He is the author, editor, or co-author of more than 100 textbooks and handbooks and has published more than 200 technical papers. In addition, he has been a Guggenheim Fellow, Fulbright Research Scholar, IEEE Fellow, and a recipient of IEEE's Baker Prize Award and Barry Carlton Award.

Zhu, Shukl, and Paul, Orthogonal Functions for Systems Identification and Control. Wang, Multilayer Recurrent Neural Networks for Synthesizing and Tuning Linear Control Systems via Pole Assignment. Rovithakis andChristodoulou, Direct and Indirect Techniques to Control Unknown Nonlinear Dynamical Systems Using Dynamical Neural Networks. Park, Choi, and Lee, A Receding Horizon Optimal Tracking Neuro-Controller for Nonlinear Dynamic Systems. Polycarpou, On-Line Approximators for Nonlinear System Identification: A Unified Approach. Billings and Chen, The Determination of Multivariable Nonlinear Models for Dynamic Systems. Kosmatopoulos and Christodoulou, High-Order Neural Network Systems in the Identification of Dynamical Systems. Porter, Liu, and Trevino, Neurocontrols for Systems with Unknown Dynamics. Napolitano and Kincheloe, On-Line Learning Neural Networks for Aircraft Autopilot and Command Augmentation Systems. Tan, Suykens, Yu, and Vandewalle, Nonlinear System Modeling.