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-propagation neural network and response surface methodology LI Ying-wei(李英伟)1, 2, PENG Jin-hui(彭金辉)1, 2, LIANG Gui-an(梁贵安)2, LI Wei(李玮)2, ZHANG Shi-min(张世敏)2 1. Faculty of Metallurgical and Energy...) neural network and response surface methodology (RSM) were used to build a predictive model of the combined effects of independent variables (the microwave power, the acting time and the rotational......
extremum response surface method (AERSM). Firstly, the AERSM was developed and its mathematical model was established based on artificial neural network, and the PSO algorithm was investigated... as well. Key words: reliability-based design optimization; flexible robot manipulator; artificial neural network; particle swarm optimization; advanced extremum response surface method 1 Introduction......
of high-order finite-impulse response filters based on neural network WANG Xiao-hua(王小华)1, 2, HE Yi-gang(何怡刚)1,LIU Mei-rong(刘美容)1 (1... 410076, China) Abstract: Four optimal approaches of high-order finite-impulse response(FIR) digital filters were developed for designing four types filters using neural network algorithms......
. Structural reliability analysis for implicit performance functions using artificial neural network [J]. Structural Safety, 2005, 27: 25-48. [16] JIN C. A new artificial neural network-based response...J. Cent. South Univ. (2012) 19: 1148-1154 DOI: 10.1007/s11771-012-1121-3 An improved adaptive response surface method for structural reliability analysis LIU Ji(刘霁)1, 2, LI Yun(李云)2 1. School......
Trajectory tracking control for underactuated surface vessels using neural network LIU Yang(刘杨)1, GUO Chen(郭晨)2 (1. School of Electronic and Information Engineering, Dalian Jiaotong University... with parameters uncertain and external disturbance problems, a stable adaptive neural network control method is proposed. Based on the diffeomorphism transformation, the new tracking variables are given......
A Comparative Study of Artificial Neural Network and Response Surface Methodology for Optimization of Friction Welding of Incoloy 800 HK.Anand1,Rishabh Shrivastava2,K.Tamilmannan1,P.Sathiya21. School...">This article deals with the optimization of process parameters for friction welding of Incoloy 800 H rod and compares the results obtained by response surface methodology(RSM) and artificial neural network(ANN......
. The reliability has been analyzed by combining the neural network method with the Monte Carlo method (MCM) and the response surface method (RSM) [15-17]. GUO and BAI [18] have introduced the least...): 3491-3507. [17] GOMES H M, AWRCUH A M. Comparison of response surface and neural network with other methods for structural reliability analysis [J]. Structural Safety, 2004, 26(1): 49-67. [18] GUO......
in deciding the weld quality. Two methods, response surface methodology and artificial neural network were used to predict the tensile strength of friction stir welded AA7039 aluminium alloy. The experiments... alloy; tensile strength; response surface methodology; artificial neural network
model was trained by the input-output data of impedance. A fuzzy neural network controller was designed to control the impedance response. The RBF neural network model was used to test the fuzzy neural...J. Cent. South Univ. Technol. (2007)01-0084-04
DOI: 10.1007/s11771-007-0017-0
Neural network modeling......Neural network modeling and control of proton exchange membrane fuel cell
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来源: 《中南大学学报(英文版)2007年第1期》——陈跃华 曹广益 朱新坚
integrates FEM equivalent model based on previous study, the artificial neural network response surface, and the genetic algorithm. First, a multi-step press bend forming FEM equivalent model was established, with which the FEM experiments designed with Taguchi method were performed. Then, the BP neural network response surface was developed with the sample data from the FEM experiments. Furthermore......