Citation: | YANG Zhen, QIAO Wen-ming, LIANG Xiao-yi. Modelling and optimization of the pore structure of carbon aerogels using an artificial neural network. New Carbon Mater., 2017, 32(1): 77-85. doi: 10.1016/S1872-5805(17)60108-2 |
Pekala R W. Organic aerogels from the polycondensation of resorcinol with formaldehyde[J]. J Mater Sci, 1989, 24: 3221-3227.
|
Pekala R W, Alviso C T, Kong F M. Aerogels derived from multifunctional organic monomers[J]. J Non-Cryst Solids, 1992, 145: 90-98.
|
Liu X M, Zhang R, Zhan L, et al. Impedance of carbon aerogel/activated carbon composites as electrodes of electrochemical capacitors in aprotic electrolyte[J]. New Carbon Mater, 2007, 22: 153-158.
|
Saliger R, Fischer U, Herta C. High surface area carbon aerogels for supercapacitors[J]. J Non-Cryst Solids, 1998, 2259: 335-342.
|
Moreno C C, Maldonado H F J. Carbon aerogels for catalysis applications: An overview[J]. Carbon, 2005, 43: 455-465.
|
Ying T Y, Yang K L, Yiacoumi S. Electrosorption of ions from aqueous solutions by nanostructured carbon aerogel[J]. J Colloid Interf Sci, 2002, 250: 18-27.
|
Timung S, Mandal T K. Prediction of flow pattern of gas-liquid flow through circular microchannel using probabilistic neural network[J]. Appl Soft Comput, 2013, 13:1674-1685.
|
Eynard J, Grieu S, Polit M. Wavelet-based multi-resolution analysis and artificial neural networks for forecasting temperature and thermal power consumption[J]. Eng Appl Artif Intel, 2011, 24: 501-516.
|
Kiranyaz S, Ince T, Yildirim A, et al. Evolutionary artificial neural networks by multi-dimensional particle swarm optimization[J]. Neural Networks, 2009, 22: 1448-1462.
|
Kuzmanovski I, Novi M, Trpkovska M. Automatic adjustment of the relative importance of different input variables for optimization of counter-propagation artificial neural networks[J]. Analytica Chem Acta, 2009, 642: 142-147.
|
Wang S C, Cong D X, Sun R J. Predicting saturates of sour vacuum gas oil using artificial neural networks and genetic algorithms[J]. Expert Syst Appl, 2010, 37: 4768-4771.
|
Plumb A P, Rowe R C, York P, et al. Optimisation of the predictive ability of artificial neural network (ANN) models: A comparison of three ANN programs and four classes of training algorithms[J]. Eur J Pharm Sci, 2005, 25: 395-405.
|
Long D H, Zhang J, Yang J H, et al. Preparation and microstructure control of carbon aerogels produced using m-cresol mediated sol-gel polymerization of phenol and furfural[J].New Carbon Mater, 2008, 23: 165-170.
|
Papadopoulos V D, Beligiannis G N, Hela D G. Combining experimental design and artificial neural networks for the determination of chlorinated compounds in fish using matrix solid-phase dispersion[J]. Appl Soft Comput, 2011, 11: 5155-5164.
|
McCulloch W, Pitt W. A logical calculus of the ideas immanent[J]. Math Biophys, 1943, 5: 115-133.
|
Khanmohammadi M, Garmarudi A B, Khoddami N, et al. A novel technique based on diffuse reflectance near-infrared spectrometry and back-propagation artificial neural network for estimation of particle size in TiO2 nano particle samples[J]. J Microchemical, 2010, 95: 337-340.
|
Hanafizadeh P, Zavasan R A, Khaki H R. An expert system for perfume selection using artificial neural network[J]. Expert Syst Appl, 2010, 37: 8879-8887.
|
Perlovsky L. Neural Networks and Intellection[M]. Oxford University Press, 2001.
|
James T L, Barkhi R, Johnson J D. Platform impact on performance of parallel genetic algorithms: Design and implementation considerations[J]. Eng Appl Artif Intel, 2006, 19: 843-856.
|
Davis L, Reinhold V N. Handbook of Genetic Algorithms[M]. New York, 1991.
|