參考文獻 |
[1]. Lee, K. Y., & El-Sharkawi, M. A. (2008). Modern heuristic optimization techniques: theory and applications to power systems (Vol. 39): John Wiley & Sons.
[2]. Park, J.-B., Lee, K.-S., Shin, J.-R., & Lee, K. Y. (2005). A particle swarm optimization for economic dispatch with nonsmooth cost functions. IEEE Transactions on Power systems, 20(1), 34-42.
[3]. 工業技術研究院. (2005). 鍋爐系統能源查核與節約能源案例手冊.
[4]. 李政道. (民102). 鍋爐蒸氣系統節能及實例. 工業技術研究院.
[5]. 李政道, & 張素美. (民101). 紡織業能源使用現況與節能實例. 工業技術研究院.
[6]. 邱志洲. (2000). 類神經網路分析 (謝邦昌, Trans.): 曉園出版社有限公司.
[7]. 紡拓會. (民105). 2016年臺灣紡織工業概況. 中華民國紡織業拓展會.
[8]. 葉怡成. (2001). 應用類神經網路 (3 ed.): 儒林圖書公司.
[9]. 葉怡成. (2003). 類神經網路模式應用與實作 (8 ed.): 儒林圖書公司.
[10]. 謝明輝. (2000). 鍋爐技術: 中華鍋爐協會.
[11]. Adewumi, A. A., Owolabi, T. O., Alade, I. O., & Olatunji, S. O. (2016). Estimation of physical, mechanical and hydrological properties of permeable concrete using computational intelligence approach. Applied Soft Computing, 42, 342-350. doi:10.1016/j.asoc.2016.02.009
[12]. Aggarwal, C. C., & Yu, P. S. (2001). Outlier detection for high dimensional data. SIGMOD Rec., 30(2), 37-46. doi:10.1145/376284.375668
[13]. Arriagada, J., Costantini, M., Olausson, P., Assadi, M., & Torisson, T. (2003). Artificial Neural Network Model for a Biomass-Fueled Boiler.
[14]. Balamurugan, I., Selladurai, V., Kulendran, B., & T. Sathyanathan, V. (2012). ANN–GA approach for predictive modeling and optimization of NOx emission in a tangentially fired boiler (Vol. 15).
[15]. Basak, D., Pal, S., & Chandra Patranabis, D. (2007). Support Vector Regression (Vol. 11).
[16]. Belosevic, S., Beljanski, V., Tomanovic, I., Crnomarkovic, N., Tucakovic, D., & Zivanovic, T. (2012). Numerical Analysis of NOx Control by Combustion Modifications in Pulverized Coal Utility Boiler. Energy & Fuels, 26(1), 425-442. doi:10.1021/ef201380z
[17]. Bennett, K. P., & Mangasarian, O. L. (1992). Robust linear programming discrimination of two linearly inseparable sets. Optimization Methods and Software, 1(1), 23-34. doi:10.1080/10556789208805504
[18]. Bäck, T. (1996). Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms: Oxford University Press.
[19]. Boeringer, D. W., & Werner, D. H. (2004). Particle swarm optimization versus genetic algorithms for phased array synthesis. IEEE Transactions on Antennas and Propagation, 52(3), 771-779. doi:10.1109/TAP.2004.825102
[20]. Boudissa, E., & Bounekhla, M. (2012). Genetic Algorithm with Dynamic Selection Based on Quadratic Ranking Applied to Induction Machine Parameters Estimation. Electric Power Components and Systems, 40(10), 1089-1104. doi:10.1080/15325008.2012.682246
[21]. Chang, C.-C., & Lin, C.-J. (2011). LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol., 2(3), 1-27. doi:10.1145/1961189.1961199
[22]. Chen, L.-Y., Hong, W.-C., Panigrahi, B., & Wei, S.-Y. (2011). SVR with Chaotic Genetic Algorithm in Taiwanese 3G Phone Demand Forecasting (Vol. 7076).
[23]. Cherkassky, V., & Ma, Y. (2003). Comparison of Model Selection for Regression. Neural Computation, 15(7), 1691-1714. doi:10.1162/089976603321891864
[24]. Chui, E. H., & Gao, H. (2010). Estimation of NOx emissions from coal-fired utility boilers. Fuel, 89(10), 2977-2984. doi:10.1016/j.fuel.2010.05.008
[25]. Cortes, C., & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20(3), 273-297. doi:10.1023/a:1022627411411
[26]. Cruz-Peragón, F., & Jiménez-Espadafor, F. J. (2007). A Genetic Algorithm for Determining Cylinder Pressure in Internal Combustion Engines. Energy & Fuels, 21(5), 2600-2607. doi:10.1021/ef0605495
[27]. De, S., Kaiadi, M., Fast, M., & Assadi, M. (2007). Development of an artificial neural network model for the steam process of a coal biomass cofired combined heat and power (CHP) plant in Sweden. Energy, 32(11), 2099-2109. doi:10.1016/j.energy.2007.04.008
[28]. Dhar, V. (2013). Data science and prediction. Commun. ACM, 56(12), 64-73. doi:10.1145/2500499
[29]. Fantozzi, F., & Desideri, U. (1998). Simulation of power plant transients with artificial neural networks: Application to an existing combined cycle. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 212(5), 299-313. doi:10.1177/095765099821200501
[30]. Fleming, P. J., & Purshouse, R. C. (2002). Evolutionary algorithms in control systems engineering: a survey. Control Engineering Practice, 10(11), 1223-1241. doi:10.1016/S0967-0661(02)00081-3
[31]. Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning: Addison-Wesley Longman Publishing Co., Inc.
[32]. Guo, M., Li, D., Du, C., Jia, Z., Qin, X., Chen, L., . . . Li, H. (2012). Prediction of the Busy Traffic in Holidays Based on GA-SVR (Vol. 169).
[33]. Hakeem, M. A., & Kamil, M. (2017). Analysis of artificial neural network in prediction of circulation rate for a natural circulation vertical thermosiphon reboiler. Applied Thermal Engineering, 112, 1057-1069. doi:10.1016/j.applthermaleng.2016.10.119
[34]. Hamid, H. A., Jenidi, Y., Thielemans, W., Somerfield, C., & Gomes, R. L. (2016). Predicting the capability of carboxylated cellulose nanowhiskers for the remediation of copper from water using response surface methodology (RSM) and artificial neural network (ANN) models. Industrial Crops and Products, 93, 108-120. doi:10.1016/j.indcrop.2016.05.035
[35]. Han, J., Kamber, M., & Pei, J. (2012). Data mining concepts and techniques, third edition. In. Waltham, Mass.: Morgan Kaufmann Publishers.
[36]. Hangos, K. M., Lakner, R., Gerzson, M., Lakner, R., & Gerzson, M. (2004). Intelligent Control Systems: An Introduction with Examples. Boston, MA: Springer.
[37]. Hao, Z., Kefa, C., & Jianbo, M. (2001). Combining neural network and genetic algorithms to optimize low NOx pulverized coal combustion. Fuel, 80(15), 2163-2169. doi:10.1016/S0016-2361(01)00104-1
[38]. Heo, J. S., Lee, K. Y., & Garduno-Ramirez, R. (2006). Multiobjective control of power plants using particle swarm optimization techniques. IEEE Transactions on Energy Conversion, 21(2), 552-561. doi:10.1109/TEC.2005.858078
[39]. Holland, J. H. (1992). Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence: MIT Press.
[40]. Hong, W.-C., Dong, Y., Chen, L.-Y., & Wei, S.-Y. (2011). SVR with hybrid chaotic genetic algorithms for tourism demand forecasting. Applied Soft Computing, 11(2), 1881-1890. doi:10.1016/j.asoc.2010.06.003
[41]. Hsu, C.-w., Chang, C.-c., & Lin, C.-J. (2003). A Practical Guide to Support Vector Classification Chih-Wei Hsu, Chih-Chung Chang, and Chih-Jen Lin.
[42]. Jain, A., Nandakumar, K., & Ross, A. (2005). Score normalization in multimodal biometric systems. Pattern Recognition, 38(12), 2270-2285. doi:10.1016/j.patcog.2005.01.012
[43]. Jain, A. K., Jianchang, M., & Mohiuddin, K. M. (1996). Artificial neural networks: a tutorial. Computer, 29(3), 31-44. doi:10.1109/2.485891
[44]. Jeong, S., Obayashi, S., & Minemura, Y. (2008). Application of hybrid evolutionary algorithms to low exhaust emission diesel engine design. Engineering Optimization, 40(1), 1-16. doi:10.1080/03052150701561155
[45]. Jianmin, L. (1993). Analysis by regression for the operational performance of typical 600 MW turbine. Journal of Gansu Sciences, 5(1), 18-23.
[46]. Jintao, H., & Xuesu, W. (1993). Derivation of characteristic equations for 300 MW extraction condensing turbine sets with multivariable linear regressing. Journal of Power Engineering, 4, 16-20.
[47]. Junliang, L., & Xinping, X. (2008, 25-27 June 2008). Multi- Swarm and Multi- Best particle swarm optimization algorithm. Paper presented at the 2008 7th World Congress on Intelligent Control and Automation.
[48]. Karonis, D., Lois, E., Zannikos, F., Alexandridis, A., & Sarimveis, H. (2003). A Neural Network Approach for the Correlation of Exhaust Emissions from a Diesel Engine with Diesel Fuel Properties (Vol. 17).
[49]. Kavaklioglu, K. (2011). Modeling and prediction of Turkey’s electricity consumption using Support Vector Regression. Applied Energy, 88(1), 368-375. doi:10.1016/j.apenergy.2010.07.021
[50]. Keerthi, S. S., & Lin, C.-J. (2003). Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Comput., 15(7), 1667-1689. doi:10.1162/089976603321891855
[51]. Kempthorne, O. (1952). The design and analysis of experiments. Oxford, England: Wiley.
[52]. Li, K., Thompson, S., & Peng, J. (2004). Modelling and prediction of NOx emission in a coal-fired power generation plant. Control Engineering Practice, 12(6), 707-723. doi:10.1016/S0967-0661(03)00171-0
[53]. Lopes, C., & Perdigão, F. (2008). Event Detection by HMM, SVM and ANN: A Comparative Study.
[54]. Lu, Y., & Roychowdhury, V. (2008). Parallel randomized sampling for support vector machine (SVM) and support vector regression (SVR) (Vol. 14).
[55]. Lv, Y., Liu, J., Yang, T., & Zeng, D. (2013). A novel least squares support vector machine ensemble model for NOx emission prediction of a coal-fired boiler. Energy, 55(Supplement C), 319-329. doi:10.1016/j.energy.2013.02.062
[56]. Ma, J. Y., & An, E. K. (2010). Optimized designing of low pressure economizer structure parameters of 350 MW power plant (Vol. 41).
[57]. Mohandes, M. A., Halawani, T. O., Rehman, S., & Hussain, A. A. (2004). Support vector machines for wind speed prediction. Renewable Energy, 29(6), 939-947. doi:10.1016/j.renene.2003.11.009
[58]. Olausson, P., Häggståhl, D., Arriagada, J., Dahlquist, E., & Assadi, M. (2003). Hybrid Model of an Evaporative Gas Turbine Power Plant Utilizing Physical Models and Artificial Neural Networks. (36843), 299-306. doi:10.1115/GT2003-38116
[59]. Owolabi, T. O., Akande, K. O., & Olatunji, S. O. (2016). Application of computational intelligence technique for estimating superconducting transition temperature of YBCO superconductors. Applied Soft Computing, 43, 143-149. doi:10.1016/j.asoc.2016.02.005
[60]. Rao, R. V., & Patel, V. K. (2010). Thermodynamic optimization of cross flow plate-fin heat exchanger using a particle swarm optimization algorithm. International Journal of Thermal Sciences, 49(9), 1712-1721. doi:10.1016/j.ijthermalsci.2010.04.001
[61]. Reynolds, C. W. (1987). Flocks, herds and schools: A distributed behavioral model. SIGGRAPH Comput. Graph., 21(4), 25-34. doi:10.1145/37402.37406
[62]. Sarkar, S., Roy, A., & Purkayastha, B. S. (2013). Application of Particle Swarm Optimization in Data Clustering: A Survey. International Journal of Computer Applications, 65(25), 38-46. doi:10.5120/11276-6010
[63]. Shi, Y., & Eberhart, R. C. (1998). Parameter selection in particle swarm optimization. Paper presented at the International conference on evolutionary programming.
[64]. Si, F., Romero, C. E., Yao, Z., Schuster, E., Xu, Z., Morey, R. L., & Liebowitz, B. N. (2009). Optimization of coal-fired boiler SCRs based on modified support vector machine models and genetic algorithms. Fuel, 88(5), 806-816. doi:10.1016/j.fuel.2008.10.038
[65]. Sivanandam, S. N., & Deepa, S. N. (2008). Introduction to Genetic Algorithms. Berlin Springer.
[66]. Smola, A. J., Sch, B., #246, & lkopf. (2004). A tutorial on support vector regression. Statistics and Computing, 14(3), 199-222. doi:10.1023/b:Stco.0000035301.49549.88
[67]. Smrekar, J., Assadi, M., Fast, M., Kuštrin, I., & De, S. (2009). Development of artificial neural network model for a coal-fired boiler using real plant data. Energy, 34(2), 144-152. doi:10.1016/j.energy.2008.10.010
[68]. Smrekar, J., Pandit, D., Fast, M., Assadi, M., & De, S. (2010). Prediction of power output of a coal-fired power plant by artificial neural network. Neural Computing and Applications, 19(5), 725-740. doi:10.1007/s00521-009-0331-6
[69]. Song, Z., & Kusiak, A. (2007). Constraint-Based Control of Boiler Efficiency: A Data-Mining Approach. IEEE Transactions on Industrial Informatics, 3(1), 73-83. doi:10.1109/TII.2006.890530
[70]. Srinivas, M., & Patnaik, L. M. (1994). Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Transactions on Systems, Man, and Cybernetics, 24(4), 656-667.
[71]. Vahid, L. K., & Nasser, S. M. (2010). Applying of genetic algorithm for optimizing methane combustion reactions. Engineering Computations, 27(4), 464-484. doi:10.1108/02644401011044577
[72]. Valente, G., Mendonca, R., Pereira, J., & Felix, L. (2012). The efficiency of electrocoagulation in treating wastewater from a dairy industry, Part I: Iron electrodes (Vol. 47).
[73]. Vapnik, V., Golowich, S. E., & Smola, A. (1996). Support vector method for function approximation, regression estimation and signal processing. Paper presented at the Proceedings of the 9th International Conference on Neural Information Processing Systems, Denver, Colorado.
[74]. Vapnik, V. N. (1995). The nature of statistical learning theory: Springer-Verlag New York, Inc.
[75]. Vapnik, V. N. (1999). An overview of statistical learning theory. IEEE Transactions on Neural Networks, 10(5), 988-999. doi:10.1109/72.788640
[76]. Varesi, K., & Radan, A. (2011). A Novel GA Based Technique for Optimizing Both the Design and Control Parameters in Parallel Passenger Hybrid Cars (Vol. 6).
[77]. Wang, C., Liu, Y., Everson, R. M., Rahat, A. A. M., & Zheng, S. (2017). Applied Gaussian Process in Optimizing Unburned Carbon Content in Fly Ash for Boiler Combustion. Mathematical Problems in Engineering, 2017, 8. doi:10.1155/2017/6138930
[78]. Wang, J., Zhang, Y., Xiong, Q., & Ding, X. (2010, 13-14 March 2010). NOx Prediction by Cylinder Pressure Based on RBF Neural Network in Diesel Engine. Paper presented at the 2010 International Conference on Measuring Technology and Mechatronics Automation.
[79]. Wei, Z., Li, X., Xu, L., & Cheng, Y. (2013). Comparative study of computational intelligence approaches for NOx reduction of coal-fired boiler. Energy, 55(Supplement C), 683-692. doi:10.1016/j.energy.2013.04.007
[80]. Wu, F., Zhou, H., Zhao, J.-P., & Cen, K.-F. (2011). A comparative study of the multi-objective optimization algorithms for coal-fired boilers. Expert Systems with Applications, 38(6), 7179-7185. doi:10.1016/j.eswa.2010.12.042
[81]. Yoo, W., Mayberry, R., Bae, S., Singh, K., He, Q., & Lillard, J. W. (2014). A Study of Effects of MultiCollinearity in the Multivariable Analysis. International journal of applied science and technology, 4(5), 9-19.
[82]. Zhang, W., Niu, P., Li, G., & Li, P. (2013). Forecasting of turbine heat rate with online least squares support vector machine based on gravitational search algorithm. Knowledge-Based Systems, 39(Supplement C), 34-44. doi:10.1016/j.knosys.2012.10.004
[83]. Zheng, L.-G., Zhou, H., Cen, K.-F., & Wang, C.-L. (2009). A comparative study of optimization algorithms for low NOx combustion modification at a coal-fired utility boiler. Expert Systems with Applications, 36(2, Part 2), 2780-2793. doi:10.1016/j.eswa.2008.01.088
[84]. Zheng, L., Yu, S., & Yu, M. (2008, 16-18 May 2008). Monitoring NOx Emissions from Coal Fired Boilers Using Generalized Regression Neural Network. Paper presented at the 2008 2nd International Conference on Bioinformatics and Biomedical Engineering.
[85]. Zhou, H., Cen, K., & Fan, J. (2004). Modeling and optimization of the NOx emission characteristics of a tangentially fired boiler with artificial neural networks. Energy, 29(1), 167-183. doi:10.1016/j.energy.2003.08.004
[86]. Zhou, H., Zhao, J., Zheng, L., Lin Wang, C., & Fa Cen, K. (2012). Modeling NOx emissions from coal-fired utility boilers using support vector regression with ant colony optimization (Vol. 25).
[87]. Zhou, H., Zheng, L., & Cen, K. (2010). Computational intelligence approach for NOx emissions minimization in a coal-fired utility boiler. Energy Conversion and Management, 51(3), 580-586. doi:10.1016/j.enconman.2009.11.002
[88]. Zhou, L., Lai, K. K., & Yu, L. (2008). Credit scoring using support vector machines with direct search for parameters selection. Soft Comput., 13(2), 149-155. doi:10.1007/s00500-008-0305-0 |