The Experts below are selected from a list of 138273 Experts worldwide ranked by ideXlab platform
Jun Wang - One of the best experts on this subject based on the ideXlab platform.
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A Neural Network Approach to nonlinear model predictive control
IECON 2011 - 37th Annual Conference of the IEEE Industrial Electronics Society, 2011Co-Authors: Zheng Yan, Jun WangAbstract:This paper proposes a Neural Network Approach to nonlinear model predictive control (NMPC). The NMPC problem is formulated as a convex programming problem via Jacobain linearization. The unknown high-order term associated with the linearization is estimated by using a feedforward Neural Network via supervised learning. The convex optimization problem involved in MPC is solved by using a recurrent Neural Network. Simulation results are provided to demonstrate the performance of the Approach.
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A Neural Network Approach to multiple criteria decision making based on fuzzy preference information
Information Sciences, 1994Co-Authors: Jun WangAbstract:Abstract Fuzzy preference information is an essential ingredient in multiple criteria decision making in fuzzy environments. This paper describes a Neural Network Approach for modeling decision makers' fuzzy preference structures. The results presented in this paper show that the proposed Neural Network Approach is capable of modeling a variety of fuzzy preference structures.
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A Neural Network Approach to modeling fuzzy preference relations for multiple criteria decision making
Computers & Operations Research, 1994Co-Authors: Jun WangAbstract:Abstract This paper describes a Neural Network Approach for modeling decision makers' fuzzy preference structures. A general feedforward Neural Network is introduced as a representation of membership function for fuzzy preference relations. A new learning algorithm is proposed for training the Neural Network to learn fuzzy preference structures. Simulation results show that the proposed Neural Network Approach is capable of modeling a variety of fuzzy preference structures.
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Multicriteria order acceptance decision support in over-demanded job shops: A Neural Network Approach
Mathematical and Computer Modelling, 1994Co-Authors: Jun Wang, Jiaqin Yang, H. LeeAbstract:Order acceptance is an important issue in job shop production systems where demand exceeds capacity. In this paper, a Neural Network Approach is developed for order acceptance decision support in job shops with machine and manpower capacity constraints. First, the order acceptance decision problem is formulated as a sequential multiple criteria decision problem. Then a Neural Network based preference model for order prioritization is described. The Neural Network based preference model is trained using preferential data derived from pairwise comparisons of a number of representative orders. An order acceptance decision rule based on the preference model is proposed. Finally, a numerical example is discussed to illustrate the use of the proposed Neural Network Approach. The proposed Neural Network Approach is shown to be a viable method for multicriteria order acceptance decision support in over-demanded job shops.
H F Zhang - One of the best experts on this subject based on the ideXlab platform.
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Performance of the Levenberg–Marquardt Neural Network Approach in nuclear mass prediction
Journal of Physics G, 2017Co-Authors: Li Hao Wang, Peng Hui Chen, H F ZhangAbstract:Resorting to a Neural Network Approach we refined several representative and sophisticated global nuclear mass models within the latest atomic mass evaluation (AME2012). In the training process, a quite robust algorithm named the Levenberg–Marquardt (LM) method is employed to determine the weights and biases of the Neural Network. As a result, this LM Neural Network Approach demonstrates a very useful tool for further improving the accuracy of mass models. For a simple liquid drop formula the root mean square (rms) deviation between the predictions and the 2353 experimental known masses are sharply reduced from 2.455 MeV to 0.235 MeV, and for the other revisited mass models, the rms is remarkably improved by about 30%.
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Performance of the Levenberg-Marquardt Neural Network Approach in nuclear mass prediction
Journal of Physics G: Nuclear and Particle Physics, 2017Co-Authors: Li Hao Wang, Peng Hui Chen, Jing Peng Yin, H F ZhangAbstract:Resorting to a Neural Network Approach we refined several representative and sophisticated global nuclear mass models within the latest atomic mass evaluation (AME2012). In the training process, a quite robust algorithm named the Levenberg–Marquardt (LM) method is employed to determine the weights and biases of the Neural Network. As a result, this LM Neural Network Approach demonstrates a very useful tool for further improving the accuracy of mass models. For a simple liquid drop formula the root mean square (rms) deviation between the predictions and the 2353 experimental known masses are sharply reduced from 2.455 MeV to 0.235 MeV, and for the other revisited mass models, the rms is remarkably improved by about 30%.
M.f. Conlon - One of the best experts on this subject based on the ideXlab platform.
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Neural Network Approach to signal modelling in power systems
IEE Proceedings - Control Theory and Applications, 1995Co-Authors: K.l. Ting, C.s. Berger, M.f. ConlonAbstract:A Neural Network Approach to a system identification problem is presented. Traditional system identification techniques require knowledge of the model structure before parameter estimation methods can be applied. This Approach requires less a priori information and the knowledge of model structure is not essential. A simulation study on a power system has demonstrated the application of this technique.
Yibing Lv - One of the best experts on this subject based on the ideXlab platform.
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a Neural Network Approach for solving linear bilevel programming problem
Knowledge Based Systems, 2010Co-Authors: Tiesong Hu, Xiang Fu, Yibing LvAbstract:A novel Neural Network Approach is proposed for solving linear bilevel programming problem. The proposed Neural Network is proved to be Lyapunov stable and capable of generating optimal solution to the linear bilevel programming problem. The numerical result shows that the Neural Network Approach is feasible and efficient.
M. Suleyman Demokan - One of the best experts on this subject based on the ideXlab platform.
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Simultaneous measurement of temperature and strain: an artificial Neural Network Approach
IEEE Photonics Technology Letters, 1998Co-Authors: Chi Chiu Chan, Wei Jin, A. Besharati Rad, M. Suleyman DemokanAbstract:In this letter, we report the use of an artificial Neural Network Approach for simultaneous recovery of information about strain and temperature from fiber optic sensors. Simulation results show that, for a particular sensor with large cross-sensitivity, temperature and strain measurement accuracy can be increased by 12 and 3 times, respectively, when compared with the matrix inversion method.
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Simultaneous recovery of temperature and strain: an artificial Neural Network Approach
Micro-optical Technologies for Measurement Sensors and Microsystems II and Optical Fiber Sensor Technologies and Applications, 1997Co-Authors: Chi Chiu Chan, Wei Jin, A. Besharati Rad, M. Suleyman DemokanAbstract:In this paper, we report an Artificial Neural Network Approach for simultaneous recovery of strain and temperature from the outputs of fiber optic sensors. Simulation results for both linear and non-linear sensing schemes are presented.
