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Implementation of Mamdami fuzzy control on a multi-DOF two-wheel inverted pendulum robot
Application of support vector machine model in wind power prediction based on particle swarm optimization
1. | School of Automation, Wuhan University of Technology, Wuhan, China |
2. | Wuhan Electric Power Dispatching and Communication Center, Wuhan, China |
References:
[1] |
W. Cheng, Convergence analysis of the numerical method for the primitive equations formulated in mean vorticity on a Cartesian grid, Discrete and Continuous Dynamical Systems - Series B, 4 (2004), 1143-1172.
doi: 10.3934/dcdsb.2004.4.1143. |
[2] |
N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines, Cambridge University Press, Cambridge, UK, 2000. |
[3] |
J. Kennedy and R. Eberhart, Swarm Intelligence, Morgan Kaufmann Publishers Inc., San Francisco, CA, 2001. |
[4] |
J. Kennedy and R. Eberhart, Particle swarm optimization, in Proceedings., IEEE International Conference on Neural Networks, 1995, Vol. 4, IEEE, 1995, 1942-1948.
doi: 10.1109/ICNN.1995.488968. |
[5] |
Y. Liu, X. F. Lu and R. M. Fang, et al., A review on wind speed forecast methods in wind power system, Power System and Clean Energy, 26 (2010), 62-66. |
[6] |
S. W. Qi, W. Q. Wang and X. Y. Zhang, Model building for wind speed and wind power prediction based on SVM, Renewable Energy Resources, 28 (2010), 25-28. |
[7] |
L. Qin, F. Z. Peng and I. J. Balaguer, Islanding control of DG in microgrids, in Power Electronics and Motion Control Conference, 2009, 450-455.
doi: 10.1109/IPEMC.2009.5157430. |
[8] |
M. Settles, An Introduction to Particle Swarm Optimization, University of Idaho, Moscow, November 2005, 1-8. |
[9] |
M. Simoes, Intelligent Based Hierarchical Control Power Electronics for Distributed Generation Systems, Power Engineering Society General Meeting, 2006.
doi: 10.1109/PES.2006.1709628. |
[10] |
P. Luís Tiago and A. C. C. F. Fernando, Adaptive time-mesh refinement in optimal control problems with state constraints, Discrete and Continuous Dynamical Systems, 32 (2015), 4553-4572.
doi: 10.3934/dcds.2015.35.4553. |
[11] |
V. N. Vapnik, Statistical Learning Theory, Wiley, New York, 1998. |
[12] |
V. N. Vapnik, The Nature of Statistical Learning Theory, Springer Press, New York, 1995.
doi: 10.1007/978-1-4757-2440-0. |
[13] |
V. N. Vapnik, S. E. Golowich and A. J. Smola, Support vectormachine for function approximation, regression estimation and signal procession, Neural Information Procession System, 9 (1996), 281-287. |
[14] |
C. S. Wang and S. X. Wang, Study on some key problems related to distributed generation systems, Automation of Electric Power Systems, 32 (2008), 1-4. |
[15] |
S. Wang, J. P. Yang and F. B. Li, et al., Short-term wind speed forecasting based on EMD and ANN, Power System Protection and Control, 40 (2012), 6-12. |
[16] |
G. Q. Wang, S. Wang and H. Y. Liu, et al., Research of short-term wind speed prediction method, Renewable Energy Resources, 32 (2014), 1134-1138. |
[17] |
J. P. Yang, Short-term Wind Speed and Power Forecasting in Wind Farm Based on ANN Combination Forecasting, Chongqing University, Chongqing, 2012. |
[18] |
Y. Zhang, Q, Zhou, C. X. Sun, S. L. Lei, Y. M. Liu and Y. Song, RBF neural network and anfis-based short-term load forecasting approach in real-time price environment, IEEE Transaction on Power Systems, 23 (2008), 853-858.
doi: 10.1109/TPWRS.2008.922249. |
show all references
References:
[1] |
W. Cheng, Convergence analysis of the numerical method for the primitive equations formulated in mean vorticity on a Cartesian grid, Discrete and Continuous Dynamical Systems - Series B, 4 (2004), 1143-1172.
doi: 10.3934/dcdsb.2004.4.1143. |
[2] |
N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines, Cambridge University Press, Cambridge, UK, 2000. |
[3] |
J. Kennedy and R. Eberhart, Swarm Intelligence, Morgan Kaufmann Publishers Inc., San Francisco, CA, 2001. |
[4] |
J. Kennedy and R. Eberhart, Particle swarm optimization, in Proceedings., IEEE International Conference on Neural Networks, 1995, Vol. 4, IEEE, 1995, 1942-1948.
doi: 10.1109/ICNN.1995.488968. |
[5] |
Y. Liu, X. F. Lu and R. M. Fang, et al., A review on wind speed forecast methods in wind power system, Power System and Clean Energy, 26 (2010), 62-66. |
[6] |
S. W. Qi, W. Q. Wang and X. Y. Zhang, Model building for wind speed and wind power prediction based on SVM, Renewable Energy Resources, 28 (2010), 25-28. |
[7] |
L. Qin, F. Z. Peng and I. J. Balaguer, Islanding control of DG in microgrids, in Power Electronics and Motion Control Conference, 2009, 450-455.
doi: 10.1109/IPEMC.2009.5157430. |
[8] |
M. Settles, An Introduction to Particle Swarm Optimization, University of Idaho, Moscow, November 2005, 1-8. |
[9] |
M. Simoes, Intelligent Based Hierarchical Control Power Electronics for Distributed Generation Systems, Power Engineering Society General Meeting, 2006.
doi: 10.1109/PES.2006.1709628. |
[10] |
P. Luís Tiago and A. C. C. F. Fernando, Adaptive time-mesh refinement in optimal control problems with state constraints, Discrete and Continuous Dynamical Systems, 32 (2015), 4553-4572.
doi: 10.3934/dcds.2015.35.4553. |
[11] |
V. N. Vapnik, Statistical Learning Theory, Wiley, New York, 1998. |
[12] |
V. N. Vapnik, The Nature of Statistical Learning Theory, Springer Press, New York, 1995.
doi: 10.1007/978-1-4757-2440-0. |
[13] |
V. N. Vapnik, S. E. Golowich and A. J. Smola, Support vectormachine for function approximation, regression estimation and signal procession, Neural Information Procession System, 9 (1996), 281-287. |
[14] |
C. S. Wang and S. X. Wang, Study on some key problems related to distributed generation systems, Automation of Electric Power Systems, 32 (2008), 1-4. |
[15] |
S. Wang, J. P. Yang and F. B. Li, et al., Short-term wind speed forecasting based on EMD and ANN, Power System Protection and Control, 40 (2012), 6-12. |
[16] |
G. Q. Wang, S. Wang and H. Y. Liu, et al., Research of short-term wind speed prediction method, Renewable Energy Resources, 32 (2014), 1134-1138. |
[17] |
J. P. Yang, Short-term Wind Speed and Power Forecasting in Wind Farm Based on ANN Combination Forecasting, Chongqing University, Chongqing, 2012. |
[18] |
Y. Zhang, Q, Zhou, C. X. Sun, S. L. Lei, Y. M. Liu and Y. Song, RBF neural network and anfis-based short-term load forecasting approach in real-time price environment, IEEE Transaction on Power Systems, 23 (2008), 853-858.
doi: 10.1109/TPWRS.2008.922249. |
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