Dự đoán sự biến đổi của oxit nitơ trong quá trình xử lý khí thải diesel bằng plasma sử dụng mạng nơ-ron nhân tạo

Alsmadi MKS, Omar KB, Noah SA et al (2009) Back propagation algorithm: the best algorithm among the multi-layer perceptron algorithm. Int J Comput Sci Netw Secur 9:378–383 Apeksha M, Rajanikanth BS (2018) Plasma/adsorbent system for NOx treatment in diesel exhaust: a case study on solid industrial wastes. Int J Environ Sci Technol. https://doi.org/10.1007/s13762-018-1776-x Balestrassi PP, Popova E, Paiva APd, Lima JWM (2009) Design of experiments on neural network’s training for nonlinear time series forecasting. Neurocomputing 72:1160–1178 Bui DT et al (2012) Landslide susceptibility assessment in the Hoa Binh province of Vietnam: a comparison of the Levenberg–Marquardt and Bayesian regularized neural networks. Geomorphology 171:12–29 Chakraborty D, Pal NR (2003) A novel training scheme for multilayered perceptrons to realize proper generalization and incremental learning. IEEE Trans Neural Netw 14:1–14 Demuth H, Beale M (2000) Neural network toolbox user’s guide Eskander GS, et al. (2007) Round trip time prediction using the symbolic function network approach. Information Technology Convergence. In: International symposium on IEEE, pp 3–7 Foresee FD, Hagan MT (1997) Gauss–Newton approximation to Bayesian learning. In: Proceedings of the 1997 international joint conference on neutral networks, vol 3, pp 1930–1935 Gadkari S, Gu S (2017) Numerical investigation of co-axial DBD: influence of relative permittivity of the dielectric barrier, applied voltage amplitude, and frequency. Phys Plasmas 24:053517 Garson GD (1991) Interpreting neural-network connection weights. AI Expert 6:46–51 Hagan MT, Menhaj MB (1994) Training feedforward networks with the Marquardt algorithm. IEEE Trans Neural Netw 5:989–993 Hagan MT, Demuth HB, Beale MH, De Jesús O (1996) Neural network design, vol 20. PWS Pub, Boston Ibrahim OM (2013) A comparison of methods for assessing the relative importance of input variables in artificial neural networks. J Appl Sci Res 9:5692–5700 Istadi I, Amin NAS (2007) Modelling and optimization of catalytic–dielectric barrier discharge plasma reactor for methane and carbon dioxide conversion using hybrid artificial neural network—genetic algorithm technique. Chem Eng Sci 62:6568–6581 Kayri M (2016) Predictive abilities of bayesian regularization and Levenberg–Marquardt algorithms in artificial neural networks: a comparative empirical study on social data. Math Comput Appl 21:20 Liu SY, Mei DH, Shen Z, Tu X (2014) Nonoxidative conversion of methane in a dielectric barrier discharge reactor: prediction of reaction performance based on neural network model. J Phys Chem C 118:10686–10693 Lourakis MIA (2005) A brief description of the Levenberg–Marquardt algorithm implemented by levmar. Found Res Technol 4:1–6 Mohammadhassani J, Khalilarya S, Solimanpur M, Dadvand A (2012) Prediction of NOx emissions from a direct injection diesel engine using artificial neural network. Model Simul Eng 2012:12 Montgomery DC (2017) Design and analysis of experiments. Wiley, Hoboken Ning Y, Liu Y, Zhang H, Ji Q (2010) Comparison of different BP neural network models for short-term load forecasting. In: IEEE international conference intelligent computing and intelligent systems (ICIS) 2010, vol 3, pp 435–438 Oguz E, Tortum A, Keskinler B (2008) Determination of the apparent rate constants of the degradation of humic substances by ozonation and modeling of the removal of humic substances from the aqueous solutions with neural network. J Hazard Mater 157:455–463 Okut H et al (2013) Predicting expected progeny difference for marbling score in Angus cattle using artificial neural networks and Bayesian regression models. Genet Sel Evol 45:34 Olden JD, Jackson DA (2002) Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks. Ecol Model 154:135–150 Olden JD, Joy MK, Death RG (2004) An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecol Model 178:389–397 Patel JL, Goyal RK (2007) Applications of artificial neural networks in medical science. Curr Clin Pharmacol 2:217–226 Rahmanian B, Pakizeh M, Mansoori SAA, Abedini R (2011) Application of experimental design approach and artificial neural network (ANN) for the determination of potential micellar-enhanced ultrafiltration process. J Hazard Mater 187:67–74 Srinivasan AD, Rajanikanth BS, Mahapatro S (2009) Corona treatment for NOx reduction from stationary diesel engine exhaust impact of nature of energization and exhaust composition. In: Proceedings electrostatics joint conference, pp 1–7 Talebizadeh P et al (2014) The role of non-thermal plasma technique in NOx treatment: a review. Renew Sustain Energy Rev 40:886–901 Toma F-L et al (2004) Neural computation to predict TiO2 photocatalytic efficiency for nitrogen oxides removal. J Photochem Photobiol A Chem 165:91–96 Yamamoto T, Okubo M, Hayakawa K, Kitaura K (1999) Towards ideal NOx/control technology using plasma-chemical hybrid process. In: Industry applications conference. 34th IAS annual meeting, 1999. Conference record of the 1999 IEEE, vol 3, pp 1495–1502