An optimal method for prediction and adjustment on byproduct gas holder in steel industry
暂无分享,去创建一个
Wei Wang | Xiaoping Zhang | Jun Zhao | Weimin Feng | Liqun Cong | Wei Wang | Jun Zhao | Liqun Cong | Xiaoping Zhang | Weimin Feng
[1] Yoshikazu Nishikawa,et al. An optimal gas supply for a power plant using a mixed integer programming model , 1991, Autom..
[2] Christian Igel,et al. Evolutionary tuning of multiple SVM parameters , 2005, ESANN.
[3] Herbert Moskowitz,et al. Modeling and optimizing a vendor managed replenishment system using machine learning and genetic algorithms , 2007, Eur. J. Oper. Res..
[4] Ping-Feng Pai,et al. Support Vector Machines with Simulated Annealing Algorithms in Electricity Load Forecasting , 2005 .
[5] Chonghun Han,et al. CONTROL OF GASHOLDER LEVEL BY TREND PREDICTION BASED ON TIME-SERIES ANALYSIS AND PROCESS HEURISTICS , 2003 .
[6] Davut Hanbay,et al. Application of least square support vector machines in the prediction of aeration performance of plunging overfall jets from weirs , 2009, Expert Syst. Appl..
[7] Gopal P. Sinha,et al. A linear programming model for distribution of electrical energy in a steel plant , 1994 .
[8] Simon Haykin,et al. Neural Networks: A Comprehensive Foundation , 1998 .
[9] J. Suykens,et al. Subspace identification of Hammerstein systems using least squares support vector machines , 2005 .
[10] Jong-Hwan Kim,et al. Quantum-inspired evolutionary algorithm for a class of combinatorial optimization , 2002, IEEE Trans. Evol. Comput..
[11] Fernando Pérez-Cruz,et al. SVM multiregression for nonlinear channel estimation in multiple-input multiple-output systems , 2004, IEEE Transactions on Signal Processing.
[12] Alexander J. Smola,et al. Learning with kernels , 1998 .
[13] Johan A. K. Suykens,et al. Financial time series prediction using least squares support vector machines within the evidence framework , 2001, IEEE Trans. Neural Networks.
[14] Milorad Bojić,et al. Energy saving does not yield CO2 emissions reductions: the case of waste fuel use in a steel mill , 2000 .
[15] I. Higashi. Energy Balance of Steel Mills and the Utilization of By-product Gases , 1982 .
[16] Senjian An,et al. Fast cross-validation algorithms for least squares support vector machine and kernel ridge regression , 2007, Pattern Recognit..
[17] Sabine Van Huffel,et al. Preoperative prediction of malignancy of ovarian tumors using least squares support vector machines , 2003, Artif. Intell. Medicine.
[18] Chonghun Han,et al. A Novel MILP Model for Plantwide Multiperiod Optimization of Byproduct Gas Supply System in the Iron- and Steel-Making Process , 2003 .
[19] Gavin C. Cawley,et al. Fast exact leave-one-out cross-validation of sparse least-squares support vector machines , 2004, Neural Networks.
[20] Johan A. K. Suykens,et al. Least Squares Support Vector Machines , 2002 .
[21] R. Poppi,et al. Least-squares support vector machines and near infrared spectroscopy for quantification of common adulterants in powdered milk. , 2006, Analytica chimica acta.
[22] Ernst Worrell,et al. Energy efficiency and carbon dioxide emissions reduction opportunities in the US iron and steel sector , 2001 .
[23] Johan A. K. Suykens,et al. Least Squares Support Vector Machines , 2002 .
[24] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[25] Keiichi Akimoto,et al. New Energy Control System at Kawasaki Steel's Mizushima Works , 1989 .
[26] Rustam M. Vahidov,et al. Application of machine learning techniques for supply chain demand forecasting , 2008, Eur. J. Oper. Res..
[27] Theodore B. Trafalis,et al. Robust classification and regression using support vector machines , 2006, Eur. J. Oper. Res..
[28] X. C. Guo,et al. A novel LS-SVMs hyper-parameter selection based on particle swarm optimization , 2008, Neurocomputing.