A self-organizing cascade neural network with random weights for nonlinear system modeling

Applied Soft Computing Journal

Volumn 42, Issue , 2016, Pages 184-193

  Li, Fanjun ^a,b,c   Qiao, Junfei ^a,c   Han, Honggui ^a,c   Yang, Cuili ^a,c  

^a BEIJING UNIVERSITY OF TECHNOLOGY   (China)

^b UNIVERSITY OF JINAN   (China)

^c BEIJING KEY LABORATORY OF COMPUTATIONAL INTELLIGENCE AND INTELLIGENT SYSTEM   (China)

Author keywords

Feedforward neural network; Nonlinear system modeling; Random weights; Self organizing cascade neural network; Wastewater treatment plant

Indexed keywords

FEEDFORWARD NEURAL NETWORKS; NEURAL NETWORKS; SEWAGE TREATMENT; WASTEWATER TREATMENT;

CASCADE NEURAL NETWORKS; GENERALIZED INVERSE; MUNICIPAL SEWAGE TREATMENTS; NONLINEAR SYSTEM MODELING; PARAMETER LEARNING; RANDOM WEIGHT; STRUCTURE-LEARNING; WASTEWATER TREATMENT PLANTS;

NONLINEAR SYSTEMS;

EID: 84958948537     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2016.01.028     Document Type: Article

References (42)

1. O. Nelles Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models 2001 Springer Science & Business Media
2. J. Ding, T. Chai, and H. Wang Offline modeling for product quality prediction of mineral processing using modeling error PDF shaping and entropy minimization IEEE Trans. Neural Netw. 22 2011 408 419
3. H.G. Han, X.L. Wu, and J.F. Qiao Real-time model predictive control using a self-organizing neural network IEEE Trans. Neural Netw. Learn. Syst. 24 2013 1425 1436
4. Y. Zhang, T. Chai, Z. Li, and et al. Modeling and monitoring of dynamic processes IEEE Trans. Neural Netw. Learn. Syst. 23 2012 277 284
5. H.G. Han, Q. Chen, and J.F. Qiao An efficient self-organizing RBF neural network for water quality prediction Neural Netw. 24 2011 717 725
6. M.H. Kim, Y.S. Kim, A.A. Prabu, and C.K. Yoo A systematic approach to data-driven modeling and soft sensing in a full-scale plant Water Sci. Technol. 60 2009 363 370
7. A.H. Gandomi, and A.H. Alavi Multi-stage genetic programming: a new strategy to nonlinear system modeling Inf. Sci. 181 2011 5227 5239
8. E. Beltrami Mathematics for Dynamic Modeling 2014 Academic Press
9. F. Ding Hierarchical multi-innovation stochastic gradient algorithm for Hammerstein nonlinear system modeling Appl. Math. Model. 37 2013 1694 1704
10. H. Han, X.L. Wu, and J.F. Qiao Nonlinear systems modeling based on self-organizing fuzzy-neural-network with adaptive computation algorithm IEEE Trans. Cybern. 44 2014 554 564
11. F. Scarselli, and A.C. Tsoi Universal approximation using feedforward neural networks: a survey of some existing methods, and some new results Neural Netw. 11 1998 15 37
12. G.A. Anastassiou Multivariate sigmoidal neural network approximation Neural Netw. 24 2011 378 386
13. B. Subudhi, and D. Jena A differential evolution based neural network approach to nonlinear system identification Appl. Soft Comput. 11 2011 861 871
14. J.F. Qiao, and H.G. Han Identification and modeling of nonlinear dynamical systems using a novel self-organizing RBF-based approach Automatica 48 2012 1729 1734
15. J.E. Meng, and S. Wu A fast learning algorithm for parsimonious fuzzy neural systems Fuzzy Sets Syst. 126 2002 337 351
16. N. Vuković, and Z. Miljković A growing and pruning sequential learning algorithm of hyper basis function neural network for function approximation Neural Netw. 46 2013 210 226
17. M.J. Er, F. Liu, and M.B. Li Self-constructing fuzzy neural networks with extended Kalman filter Int. J. Fuzzy Syst. 12 2010 66 72
18. H.G. Han, L.D. Wang, and J.F. Qiao Efficient self-organizing multilayer neural network for nonlinear system modeling Neural Netw. 43 2013 22 32
19. W. Yu, K. Li, and X. Li Automated nonlinear system modeling with multiple neural networks Int. J. Syst. Sci. 42 2011 1683 1695
20. B.M. Wilamowski Neural network architectures and learning algorithms IEEE Ind. Electron. Mag. 3 2009 56 63
21. D. Hunter, H. Yu, M.S. Pukish, and et al. Selection of proper neural network sizes and architectures - a comparative study IEEE Trans. Ind. Inform. 8 2012 228 240
22. B.M. Wilamowski, and H. Yu Improved computation for Levenberg-Marquardt training IEEE Trans. Neural Netw. 21 2010 930 937
23. B.M. Wilamowski, and H. Yu Neural network learning without backpropagation IEEE Trans. Neural Netw. 21 2010 1793 1803
24. E. Scott, and L. Christian The cascade-correlation learning architecture Adv. Neural Inf. Process. Syst. 1992 524 532
25. F. Li, and Y. Li Cascade-correlation algorithm with trainable activation functions Comput. Inf. Sci. 4 2011 28 37
26. J.J.T. Lahnajärvi, M.I. Lehtokangas, and J.P.P. Saarinen Evaluation of constructive neural networks with cascaded architectures Neurocomputing 48 2002 573 607
27. G. Huang, S. Song, and C. Wu Orthogonal least squares algorithm for training cascade neural networks IEEE Trans. Circuits Syst.: Regul. Pap. 59 2012 2629 2637
28. M. Alhamdoosh, and D. Wang Fast decorrelated neural network ensembles with random weights Inf. Sci. 264 2014 104 117
29. B. Igelnik, and Y.H. Pao Stochastic choice of basis functions in adaptive function approximation and the functional-link net IEEE Trans. Neural Netw. 6 1995 1320 1329
30. W.F. Schmidt, M.A. Kraaijveld, and R.P.W. Duin Feedforward neural networks with random weights Proc. of 11th IAPR Int. Conf. on Pattern Recog., Conf. B: Pattern Recognition Methodology and Systems, vol. 2 1992 1 4
31. S. Scardapane, D. Wang, M. Panella, and et al. Distributed learning for random vector functional-link networks Inf. Sci. 301 2015 271 284
32. Y.H. Pao, G.H. Park, and D.J. Sobajic Learning and generalization characteristics of the random vector functional-link net Neurocomputing 6 1994 163 180
33. W. Dai, Q. Liu, and T. Chai Particle size estimate of grinding processes using random vector functional link networks with improved robustness Neurocomputing 169 2015 361 372
34. P. Zhou, M. Yuan, H. Wang, and T. Chai Multivariable dynamic modeling for molten iron quality using online sequential random vector functional-link networks with self-feedback connections Inf. Sci. 325 2015 237 255
35. R. Cass, and B. Radl Adaptive process optimization using functional-link networks and evolutionary optimization Control Eng. Pract. 4 1996 1579 1584
36. F. Cao, H. Ye, and D. Wang A probabilistic learning algorithm for robust modeling using neural networks with random weights Inf. Sci. 313 2015 62 78
37. J. Qiao, F. Li, H. Han, and W. Li Constructive algorithm for fully connected cascade feedforward neural networks Neurocomputing 182 2016 154 164 10.1016/j.neucom.2015.12.003
38. M.M. Islam, M.A. Sattar, M.F. Amin, and et al. A new constructive algorithm for architectural and functional adaptation of artificial neural networks IEEE Trans. Syst. Man Cybern. Part B: Cybern. 39 2009 1590 1605
39. H. Han, and J. Qiao A self-organizing fuzzy neural network based on a growing-and-pruning algorithm IEEE Trans. Fuzzy Syst. 18 2010 1129 1143
40. H.G. Han, and J.F. Qiao A structure optimization algorithm for feedforward neural network construction Neurocomputing 99 2013 347 357
41. N. Vuković, and Z. Miljković Robust sequential learning of feedforward neural networks in the presence of heavy-tailed noise Neural Netw. 63 2015 31 47
42. Y. Xue, L. Yang, and S. Haykin Decoupled echo state networks with lateral inhibition Neural Netw. 20 2007 365 376