Learning Automata and Stochastic Optimization

• Κύριοι συγγραφείς: Poznyak, A.S (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut), Najim, K. (http://id.loc.gov/vocabulary/relators/aut)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 1997.
• Έκδοση: 1st ed. 1997.
• Σειρά: Lecture Notes in Control and Information Sciences, 225
• Θέματα: Control engineering.; Robotics.; Mechatronics.; Vibration.; Dynamical systems.; Dynamics.; Statistical physics.; Control, Robotics, Mechatronics.; Vibration, Dynamical Systems, Control.; Complex Systems.; Statistical Physics and Dynamical Systems.

 In the last decade there has been a steadily growing need for and interest in computational methods for solving stochastic optimization problems with or wihout constraints. Optimization techniques have been gaining greater acceptance in many industrial applications, and learning systems have made a significant impact on engineering problems in many areas, including modelling, control, optimization, pattern recognition, signal processing and diagnosis. Learning automata have an advantage over other methods in being applicable across a wide range of functions. Featuring new and efficient learning techniques for stochastic optimization, and with examples illustrating the practical application of these techniques, this volume will be of benefit to practicing control engineers and to graduate students taking courses in optimization, control theory or statistics. 

 Stochastic optimization -- On learning automata -- Unconstrained optimization problems -- Constrained optimization problems -- Optimization of nonstationary functions.