References

  1. H.X. Du, F.S. Li, Z.J. Yu, C.H. Feng, W.H. Li, Nitrification and denitrification in two-chamber microbial fuel cells for treatment of wastewater containing high concentrations of ammonia nitrogen, Environ. Technol., 37 (2016) 1232–1239.
  2. S.F. Kosari, B. Rezania, K.V. Lo, D.S. Mavinic, Operational strategy for nitrogen removal from centrate in a two-stage partial nitrification – anammox process, Environ. Technol., 35 (2014) 1110–1120.
  3. Y.Q. Yao, D.F. Lu, Z.M. Qi, S.H. Xia, Miniaturized optical system for detection of ammonia nitrogen in water based on gas-phase colorimetry, Anal. Lett., 45 (2012) 2176–2184.
  4. I.M. Valente, H.M. Oliveira, C.D. Vaz, R.M. Ramos, Determination of ammonia nitrogen in solid and liquid highcomplex matrices using one-step gas-diffusion microextraction and fluorimetric detection, Talanta, 167 (2017) 747–753.
  5. H. Haimi, M. Mulas, F. Corona, R. Vahala, Data-derived softsensors for biological wastewater treatment plants: an overview, Environ. Modell. Software, 47 (2013) 88–107.
  6. H.G. Han, Y. Li, Y.N. Guo, J.F. Qiao, A soft computing method to predict sludge volume index based on a recurrent selforganizing neural network, Appl. Soft Comput., 38 (2016) 477–486.
  7. X.F. Yuan, Z.Q. Ge, Z.H. Song, Y.L. Wang, C.H. Yang, H.W. Zhang, Soft sensor modeling of nonlinear industrial processes based on weighted probabilistic projection regression, IEEE Trans. Instrum. Meas., 66 (2017) 837–845.
  8. S. Yin, X.W. Li, H.J. Gao, O. Kaynak, Data-based techniques focused on modern industry: an overview, IEEE Trans. Ind. Electron., 62 (2015) 657–667.
  9. J.B. Zhang, Z.H. Deng, K.S. Choi, S.T. Wang, Data-driven elastic fuzzy logic system modeling: constructing a concise system with human-like inference mechanism, IEEE Trans. Fuzzy Syst., 26 (2018) 2160–2173.
  10. J.F. Canete, R. Baratti, M. Mulas, A. Ruano, Soft-sensing estimation of plant effluent concentrations in a biological wastewater treatment plant using an optimal neural network, Expert Syst. Appl., 63 (2016) 8–19.
  11. M.F. Nezhad, N. Mehrdadi, A. Torabian, S. Behboudian, Artificial neural network modeling of the effluent quality index for municipal wastewater treatment plants using quality variables: south of Tehran wastewater treatment plant, J. Water Supply Res. Technol. AQUA, 65 (2016) 18–27.
  12. M. Bagheri, S.A. Mirbagheri, Z. Bagheri, A.M. Kamarkhanie, Modeling and optimization of activated sludge bulking for a real wastewater treatment plant using hybrid artificial neural networks-genetic algorithm approach, Process Saf. Environ. Prot., 95 (2015) 12–25.
  13. D. Dovžan, V. Logar, I. Škrjanc, Implementation of an evolving fuzzy model (eFuMo) in a monitoring system for a waste-water treatment process, IEEE Trans. Fuzzy Syst., 23 (2015) 1761–1776.
  14. M.M. Ebadzadeh, A.S. Badr, IC-FNN: a novel fuzzy neural network with interpretable intuitive and correlated-contours fuzzy rules for function approximation, IEEE Trans. Fuzzy Syst., 26 (2018) 1288–1302.
  15. J.J. Rubio, SOFMLS: online self-organizing fuzzy modified leastsquares network, IEEE Trans. Fuzzy Syst., 17 (2009) 1296–1309.
  16. N. Wang, M.J. Er, X.Y. Meng, A fast and accurate online selforganizing scheme for parsimonious fuzzy neural networks, Neurocomputing, 72 (2009) 3818–3829.
  17. J.F. Qiao, W. Li, H.G. Han, Soft computing of biochemical oxygen demand using an improved T–S fuzzy neural network, Chin. J. Chem. Eng., 22 (2014) 1254–1259.
  18. C.F. Juang, C.D. Hsieh, A fuzzy system constructed by rule generation and iterative linear SVR for antecedent and consequent parameter optimization, IEEE Trans. Fuzzy Syst., 20 (2012) 372–384.
  19. M.M. Ebadzadeh, A.S. Badr, CFNN: correlated fuzzy neural network, Neurocomputing, 148 (2015) 430–444.
  20. M.Z. Huang, Y.W. Ma, J.Q. Wan, X.H. Chen, A sensor-software based on a genetic algorithm-based neural fuzzy system for modeling and simulating a wastewater treatment process, Appl. Soft Comput., 27 (2015) 1–10.
  21. L. Teslic, B. Hartmann, O. Nelles, I. Škrjanc, Nonlinear system identification by Gustafson–Kessel fuzzy clustering and supervised local model network learning for the drug absorption spectra process, IEEE Trans. Neural Networks Learn. Syst., 22 (2011) 1941–1951.
  22. H. Malek, M.M. Ebadzadeh, M. Rahmati, Three new fuzzy neural networks learning algorithms based on clustering, training error and genetic algorithm, Appl. Intell., 37 (2012) 280–289.
  23. M. Seera, C.P. Lim, C.K. Loo, H. Singh, A modified fuzzy min–max neural network for data clustering and its application to power quality monitoring, Appl. Soft Comput., 28 (2015) 19–29.
  24. J.J. Tang, F. Liu, W.H. Zhang, R.M. Ke, Y.J. Zou, Lane-changes prediction based on adaptive fuzzy neural network, Expert Syst. Appl., 91 (2018) 452–463.
  25. J.J. Tang, F. Liu, Y.J. Zou, W.B. Zhang, Y.H. Wang, An improved fuzzy neural network for traffic speed prediction considering periodic characteristic, IEEE Trans. Intell. Transp. Syst., 18 (2017) 2340–2350.
  26. J.J. Tang, Y.J. Zou, J. Ash, S. Zhang, F. Liu, Y.H. Wang, Travel time estimation using freeway point detector data based on evolving fuzzy neural inference system, PLoS One, 11 (2016) e0147263.
  27. R.D. Zhang, J.L. Tao, A nonlinear fuzzy neural network modeling approach using an improved genetic algorithm, IEEE Trans. Ind. Electron., 65 (2018) 5882–5892.
  28. Y.Y. Lin, J.Y. Chang, C.T. Lin, Identification and prediction of dynamic systems using an interactively recurrent self-evolving fuzzy neural network, IEEE Trans. Neural Networks Learn Syst., 24 (2013) 310–321.
  29. M. Prasad, C.T. Lin, D.L. Li, C.T. Hong, W.P. Ding, J.Y. Chang, Soft-boosted self-constructing neural fuzzy inference network, IEEE Trans. Syst. Man Cybern. Syst., 47 (2017) 584–588.
  30. S.Q. Wu, M.J. Er, Dynamic fuzzy neural networks-a novel approach to function approximation, IEEE Trans. Syst. Man Cybern. Part B Cybern., 30 (2000) 358–364.
  31. S.Q. Wu, M.J. Er, Y. Gao, A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks, IEEE Trans. Fuzzy Syst., 9 (2001) 578–594.
  32. X.J. Ma, J. Yu, Q.L. Dong, A generalized dynamic fuzzy neural network based on singular spectrum analysis optimized by brain storm optimization for short-term wind speed forecasting, Appl. Soft Comput., 54 (2017) 296–312.
  33. J.C. Bezdek, R. Ehrlich, W. Full, FCM: the fuzzy c-means clustering algorithm, Comput. Geosci., 10 (1984) 191–203.
  34. D. Graves, W. Pedrycz, Kernel-based fuzzy clustering and fuzzy clustering: a comparative experimental study, Fuzzy Sets Syst., 161 (2010) 522–543.
  35. M.G. Gong, Y. Liang, J. Shi, W.P. Ma, J.J. Ma, Fuzzy c-means clustering with local information and kernel metric for image segmentation, IEEE Trans. Image Process., 22 (2013) 573–584.
  36. K.P. Lin, A novel evolutionary kernel intuitionistic fuzzy c-means clustering algorithm, IEEE Trans. Fuzzy Syst., 22 (2014) 1074–1087.
  37. B.M. Wilamowski, H. Yu, Improved computation for Levenberg–Marquardt training, IEEE Trans. Neural Networks Learn Syst., 21 (2010) 930–937.
  38. H.G. Han, L.M. Ge, J.F. Qiao, An adaptive second order fuzzy neural network for nonlinear system modeling, Neurocomputing, 214 (2016) 837–847.
  39. Z. Wang, J.S. Chu, Y Song. Y.J. Cui, H. Zhang, X.Q. Zhao, Z.H. Li, J.M. Yao, Influence of operating conditions on the efficiency of domestic wastewater treatment in membrane bioreactors, Desalination, 245 (2009) 73–81.
  40. F.J. Chang, Y.H. Tsai, P.A. Chen, A. Coynel, G. Vachaud, Modeling water quality in an urban river using hydrological factors–data driven approaches, J. Environ. Manage., 151 (2015) 87–96.
  41. E. Lee, S. Han, H. Kim, Development of software sensors for determining total phosphorus and total nitrogen in waters, Int. J. Environ. Res. Public Health, 10 (2013) 219–236.