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Accession number: 20110713657302 Title: The application of neural network in nonlinear system Authors: Shi, Lei1, 2 Email author shileiboda@163.com; Wang, Xing-Cheng1 Email author xc_wang@dl.cn Author affiliation: 1 Information Science and Technology College, Dalian Maritime University, Dalian 116026, China 2 College of Information Science and Engineering, Bohai University, Jinzhou 121000, China Corresponding author: Shi, L. (shileiboda@163.com) Source title: Advanced Materials Research Abbreviated source title: Adv. Mater. Res. Volume: 179-180 Monograph title: Materials Science and Engineering Issue date: 2011 Publication year: 2011 Pages: 128-134 Language: English ISSN: 10226680 ISBN-13: 9783037850169 Document type: Conference article (CA) Conference name: 2010 International Conference on Materials Science and Engineering Science, ICMSES 2010 Conference date: December 11, 2010 - December 12, 2010 Conference location: Shenzhen, China Conference code: 83738 Sponsor: International Communication Science Association; Huazhong University of Science and Technology Publisher: Trans Tech Publications, P.O. Box 1254, Clausthal-Zellerfeld, D-38670, Germany Abstract: Neural network theory is widely applied to predictive control system because of its superiority in dealing with nonlinearities therein. Meanwhile, various algorithms for neural network predictive control have been put forward. The paper investigates the application of neural network-based control in nonlinear system. Especially, some current important nerual network-based controls are remarked and the developments are prospected. © (2011) Trans Tech Publications, Switzerland. Number of references: 22 Main heading: Neural networks Controlled terms: Control nonlinearities - Feedback control - Nonlinear systems - Predictive control systems Uncontrolled terms: Network-based control - Non-Linearity - Predictive control Classification code: 723.4 Artificial Intelligence - 731.1 Control Systems DOI: 10.4028/www.scientific.net/AMR.179-180.128 Database: Compendex Compilation and indexing terms, © 2013 Elsevier Inc. |
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