The authors proposed what they call new method to design ADCES using RBF instead of traditional method called 'the first principle models.' The reviewer found that the paper was written in much poorer expressions but highly depending on the Xing et al.'s in 2008 paper appeared in the17th IFAC World Congress, including the same photo, similar phrases, the identical experimental setups, etc. Nevertheless the Xing's et al.'s paper was never referred to in this paper. Not in the list of Reference either. It's not good at all to copy from other papers without referring to it, if not a total plagiarism, in this case. In addition, there are not a few papers already published regarding ADCES with a Neural Network including RBF. Also the topic of 'RBF with GA' is quite popular. Therefore, although authors proudly wrote, "This is the first application of RBFNN to model an electrohydraulic system intently and intensively with genetic algorithm," it doesn't seem to be new to the reviewer. ================================================================================================== sent on 24 May 2010 eng mistake like "showed" instead "shown" or "corresponding" instead of "corresponds" if not a plagiarithm Modeling and Identification of Electrohydraulic System and Its Application Xing, Zongyi, Gao, Qiang, Wu, Yingying 2008-07-06 Authors: Xing, Zongyi, Gao, Qiang, Wu, Yingying Abstract: In general, the first and the most important step in system analysis, prediction and control is the proper model of the system. In order to design the controller of nonlinear electrohydraulic system, several modeling techniques are proposed: the transfer function of the electrohydraulic system is identified using first-principle method, and the intelligent models are built by fuzzy modeling and neural networks. First, -------------------------------------------------------------------------------------------------- the automatic depth control electrohydraulic system (ADCES) of a certain type of weapon is introduced, and how to obtain the input-output data is proposed. -------------------------------------------------------------------------------------------------- Then, three modeling algorithms are detailed, including transfer function, fuzzy system and neural networks. Finally, five models are identified based on the ADCES; and the analysis of the obtained models lays the foundation of the controller design. Keywords: Nonlinear system identification; Identification for control Identifier: 10.3182/20080706-5-KR-1001.0234 Conference: Proceedings of the 17th IFAC World Congress, 2008 Location: COEX, Korea, South Start Date: Sun Jul 06 2008 - End Date: Fri Jul 11 2008 just a simple search via google with keywards ADCES, RBF,GA ================================================================================================== Proceedings of the 17th IFAC World Congress, 2008 World Congress, Volume. 17 Part 1 google IFAC-Papers OnLine: World Congress Proceedings of the 17th IFAC ... First, the automatic depth control electrohydraulic system (ADCES) of a certain type of weapon is introduced, and how to obtain the input-output data is proposed. Then, three modeling algorithms are detailed, including transfer function ... www.ifac-papersonline.net/World...of.../more63.html - キャッシュ Full-Text Search： 《Journal of System Simulation》 2009-06Add to Favorite Get Latest Update Modeling of Electrohydraulic System and Its ApplicationXING Zong-yi1,ZHANG Yuan 1,HOU Yuan-long1,JIA Li-min2(1.School of Mechanical Engineering,Nanjing University of Science and Technology,Nanjing 210094,China;2.School of Traffic and Transportation,Beijing Jiaotong University,Beijing 100044,China) Several modeling techniques were proposed to identify the electrohydraulic system:the transfer function is constructed using first-principle method,and the intelligent models are built by fuzzy modeling and neural networks.First,the automatic depth control electrohydraulic system(ADCES) of a certain type of weapon was introduced,and how to obtain the input-output data was proposed.Then,three modeling algorithms were detailed,including transfer function,fuzzy system and neural networks.Finally,five models were identified based on the ADCES;and the analysis of the obtained models lays the foundation of the controller design. 【Key Words】： electrohydraulic system modeling fuzzy model neural networks 【Fund】： 国家自然科学基金(60332020);; ?道交通控制与安全国家重点??室(北京交通大学)?放??基金(SKL2008K010);; 南京理工大学科技?展基金(XKF09003) 【CateGory Index】： TH137.9 【DOI】： CNKI:SUN:XTFZ.0.2009-06-058 again google electrohydraulic RBF neural-networks genetic-algorithm -------------------------------------------------------------------------------------------------- Radial basis function neural network for hydrologic inversion: an appraisal with classical and spatio-temporal geostatistical techniques in the context of site characterization 雑誌 Stochastic Environmental Research and Risk Assessment 出版社 Springer Berlin / Heidelberg ISSN 1436-3240 (Print) 1436-3259 (Online) 号 Volume 23, Number 7 / 2009年10月 カテゴリー Original Paper DOI 10.1007/s00477-008-0262-2 ページ 933-945 Subject Collection 地球および環境科学 SpringerLink 日付 2008年9月17日 印を付けたアイテムに追加 買い物カゴに追加 保存済みアイテムに追加 Permissions & Reprints この記事を推薦 PDF (600.3 KB)HTML Original Paper Radial basis function neural network for hydrologic inversion: an appraisal with classical and spatio-temporal geostatistical techniques in the context of site characterization Amvrossios C. Bagtzoglou1 and Faisal Hossain2 (1) Department of Civil and Environmental Engineering, University of Connecticut, U2037, Storrs, CT 06269-2037, USA (2) Department of Civil and Environmental Engineering, Tennessee Technological University, Cookeville, TN 38505, USA Published online: 17 September 2008 Abstract This paper investigates three techniques for spatial mapping and the consequential hydrologic inversion, using hydraulic conductivity (or transmissivity) and hydraulic head as the geophysical parameters of concern. The data for the study were obtained from the Waste Isolation and Pilot Plant (WIPP) site and surrounding area in the remote Chihuahuan Desert of southeastern New Mexico. The central technique was the Radial Basis Function algorithm for an Artificial Neural Network (RBF-ANN). An appraisal of its performance in light of classical and temporal geostatistical techniques is presented. Our classical geostatistical technique of concern was Ordinary Kriging (OK), while the method of Bayesian Maximum Entropy (BME) constituted an advanced, spatio-temporal mapping technique. A fusion technique for soft or inter-dependent data was developed in this study for use with the neural network. It was observed that the RBF-ANN is capable of hydrologic inversion for transmissivity estimation with features remaining essentially similar to that obtained from kriging. The BME technique, on the other hand, was found to reveal an ability to map localized lows and highs that were otherwise not as apparent in OK or RBF-ANN techniques. Keywords Radial basis functions - Artificial neural networks - Bayesian maximum entropy - Spatial interpolation - Geostatistics - Kriging - Site characterization - Waste Isolation Pilot Plant -------------------------------------------------------------------------------------------------- Application of immune neural network for electro-hydraulic servo valve fault diagnosis 4620716 abstract Content is outside your subscription Sign In:Full text access is unavailable with your individual subscription. If your institution subscribes to IEEE Xplore, access may be available by signing in with your institutional credentials. Contact your librarian or informational professional for more details. User Name Password Forgot Username/Password? Lian-Dong Fu; Kui-Sheng Chen; Shu-Guang Fu; Long-Yuan Liu; Jin Zhu; Coll. of Machinery & Autom., Wuhan Univ. of Sci. & Technol., Wuhan This paper appears in: Machine Learning and Cybernetics, 2008 International Conference on Issue Date: 12-15 July 2008 On page(s): 1898 - 1902 Location: Kunming Print ISBN: 978-1-4244-2095-7 INSPEC Accession Number: 10206917 Digital Object Identifier: 10.1109/ICMLC.2008.4620716 Date of Current Version: 05 9a?? 2008 Abstract Using the biological immunology principle unifies the neural network and the immunity algorithm to form the immunity neural network, which is applied to electro-hydraulic servo valve breakdown diagnosis. The result indicated that, the immunity neural network can identify many kinds of failures pattern recognition accurately by the smaller network scale, and has the high efficiency, good fault-tolerant performance and formidable auto-adapted ability. Title page for 89342002 - [ このページを訳す ]PC Chen 著 - 2008 - 関連記事 3 Feb 2009 ... 4.2 Radial Basis Function Network100 4.3 Development of a GA-Based Neural Network Control Theory103 .... [46]C. L. Hwang, “Neural-Network-Based Variable Structure Control of Electrohydraulic Servosystems Subject to Huge ... thesis.lib.ncu.edu.tw/ETD-db/ETD.../view_etd?URN... - キャッシュ - 類似ページ ==================================================================================================