[1] S.R. Ponnuswamy, S.L. Shah, C.A. Kiparissides, Computer optimal control of batch polymerization reactors, Ind. Eng. Chem. Res. 50 (11) (1987) 2229-2236.
[2] J.R. Banga, K.J. Versyck, J.F. VanImpe, Computation of optimal identification experiments for nonlinear dynamic process models: A stochastic global optimization approach, Ind. Eng. Chem. Res. 41 (10) (2002) 2425-2430.
[3] J.S. Chang, S.C. Lu, Y.L. Chiu, Dynamic modeling of batch polymerization reactors via the hybrid neural-network rate-function approach, Chem. Eng. J. 130 (1) (2007) 19-28.
[4] C. Shene, C. Diez, S. Bravo, Neural networks for the prediction of the state of Zymomonas mobilis CP4 batch fermentations, Comput. Chem. Eng. 23 (8) (1999) 1097-1108.
[5] C. Shene, C. Diez, S. Bravo, S. Alasheh, F.S. Mjalli, H.E. Alfadala, Forecasting influent-effluent wastewater treatment plants using time series analysis and artificial neural network, Comput. Chem. Eng. 23 (2007) 1097-1108.
[6] D.E. Rumelhart, G.E. Hintont, R.J. Williams, Learning representations by backpropagating errors, Nature 323 (9) (1986) 533-636.
[7] J. Thinault, V.V. Breusegem, A. Cheruy, On-line prediction of fermentation variables using neural networks, Biotechnol. Bioeng. 36 (10) (1990) 1041-1048.
[8] D.C. Psichogios, L.H. Ungar, A hybrid neural network-first principles approach to process modeling, AICHE J. 38 (10) (1992) 1499-1511.
[9] I.M. Galván, J.M. Zaldívar, H. Hernández, E. Molga, The use of neural networks for fitting complex kinetic data, Comput. Chem. Eng. 20 (12) (1996) 1451-1465.
[10] J.F. Pollard, M.R. Broussard, D.B. Garrison, K.Y. San, Process identification using neural networks, Comput. Chem. Eng. 16 (4) (1992) 253-270.
[11] W. Bian, D. Tao, Max-min distance analysis by using sequential SDP relaxation for dimension reduction, IEEE Trans. Pattern Anal. Mach. Intell. 33 (5) (2011) 1037-1050.
[12] H.W. Wang, R. Guan, J.J. Wu, CIPCA: complete-information-based principal component analysis for interval-valued data, Neurocomputing 86 (1) (2012) 158-169.
[13] A. Namphol, S.H. Chin, M. Arozullah, Image compression with a hierarchical neural network, IEEE Trans. Aerosp. Electron. Syst. 32 (1996) 326-338.
[14] H. Bourlard, Y. Kamp, Auto-association by multilayer perceptrons and singular value decomposition, Biol. Cybern. 59 (4) (1988) 291-294.
[15] P. Baldi, K. Hornik, Neural networks and principal component analysis: Learning from examples without local minima, Neural Netw. 2 (1) (1989) 53-58.
[16] Y. Cai, Y. Xu, Q.X. Zhu, Data filtering method and application based on autoassociative neural network, Comput. Appl. Chem. 26 (5) (2009) 673-676.
[17] M.A. Aly, A.F. Atiya, Novelmethods for the feature subset ensembles approach, Int. J. Artif. Intell. Mach. Learn. 6 (4) (2006) 21-27.
[18] L. Rokach, O. Maimon, Feature set decomposition for decision trees, Intell. Data Anal. 9 (2) (2005) 131-158.
[19] L. Rokach, O. Maimon, Decision-tree instance-space decomposition with grouped gain-ratio, Inf. Sci. 177 (17) (2007) 3592-3612.
[20] X.Y. Zheng, Y. Xu, Q.X. Zhu, S.W. Peng, Data attributes decomposition-based hierarchical neural network, Intell. Comput. Intell. Syst. (ICIS) 1 (2010) 343-347.
[21] D.X. Qiu, A comparative study of the K-means algorithm and the normal mixture model for clustering: bivariate homoscedastic case, J. Stat. Plan. Infer. 140 (7) (2010) 1701-1711.
[22] W. Cai, Extension management engineering and applications, Int. J. Oper. Quant. Manage. 5 (1) (1999) 59-72.
[23] W. Cai, Extension theory and its application, Chin. Sci. Bull. 44 (17) (1999) 1538-1548.
[24] Y. Xu, Q.X. Zhu, A new extension theory-based production operation method in industrial process, Chin. J. Chem. Eng. 21 (1) (2013) 44-54.
[25] M.H. Wang, C.P. Hung, Extension neural network and its applications, Neural Netw. 16 (5) (2003) 779-784.
[26] M.H. Wang, Extension neural network-type 2 and its applications, IEEE Trans. Neural Networks 16 (6) (2005) 1352-1361.
[27] M.H. Wang, Extension neural network-type 3, Lect. Notes Comput. Sci 3496 (1) (2005) 503-508.
[28] A.K. Giri, R.K. Patel, S.S. Mahapatra, Artificial neural network (ANN) approach for modelling of arsenic (III) biosorption fromaqueous solution by living cells of Bacillus cereus biomass, Chem. Eng. J. 178 (2011) 15-25.
[29] Y. Xu, Q.X. Zhu, Research and implementation of decreasing the acetic acid consumption in purified terephthalic acid solvent system, Chin. J. Chem. Eng. 16 (4) (2008) 650-655.
[30] K.A. Al-Shayji, Modeling, Simulation and Optimization of Large-scale Commercial Desalination Plant(PhD Thesis) Virginia Polytechnic Institute and State University, VA, USA, 1998. |