Efficient self-organizing multilayer neural network for nonlinear system modeling

Neural Networks

Volumn 43, Issue , 2013, Pages 22-32

  Han, Hong Gui ^a   Wang, Li Dan ^a   Qiao, Jun Fei ^a  

^a BEIJING UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Adaptive connecting and pruning algorithm; Automatic axon neural network; Feedforward computation; Information theory; Modeling

Indexed keywords

BENCH-MARK PROBLEMS; BETTER PERFORMANCE; FEED-FORWARD; MIXED-MODE OPERATIONS; NONLINEAR FUNCTIONS; NONLINEAR SYSTEM MODELING; NUMBER OF HIDDEN NEURONS; PRUNING ALGORITHMS;

INFORMATION THEORY; LEARNING ALGORITHMS; MODELS; MULTILAYER NEURAL NETWORKS; NETWORK ARCHITECTURE; NETWORK PERFORMANCE; NEURONS;

NONLINEAR SYSTEMS;

ADAPTIVE CONNECTING AND PRUNING ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOMATIC AXON NEURAL NETWORK; FEEDFORWARD COMPUTATION; INTERMETHOD COMPARISON; LEARNING ALGORITHM; NONLINEAR SYSTEM; PRIORITY JOURNAL; QUALITY CONTROL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; AXONS; NEURAL NETWORKS (COMPUTER); NEURONS; NONLINEAR DYNAMICS;

EID: 84875257054     PISSN: 08936080     EISSN: 18792782     Source Type: Journal    
DOI: 10.1016/j.neunet.2013.01.015     Document Type: Article

References (49)

1. Bergmann U., Malsburg C. Self-organization of topographic bilinear networks for invariant recognition. Neural Computation 2011, 23(11):2770-2797.
2. Briggs F., Callaway E.M. Laminar patterns of local excitatory input to layer 5 neurons in macaque primary visual cortex. Cerebral Cortex 2004, 15(5):479-488.
3. Carandini M., Heeger D.J. Normalization as a canonical neural computation. Nature Reviews Neuroscience 2012, 13(1):51-62.
4. Dierks T., Jagannathan S. Online optimal control of affine nonlinear discrete-time systems with unknown internal dynamics by using time-based policy update. IEEE Transactions on Neural Networks and Learning Systems 2012, 23(7):1118-1129.
5. Ferrari S., Jensenius M. A constrained optimization approach to preserving prior knowledge during incremental training. IEEE Transactions on Neural Networks 2008, 19(6):996-1009.
6. Finnoff W. Diffusion approximations for the constant learning rate BP algorithm and resistance to local minima. Neural Computation 1994, 6(2):285-295.
7. Goltsev A., Gritsenko V. Investigation of efficient features for image recognition by neural networks. Neural Networks 2012, 28(1):15-23.
8. Han H.G., Chen Q.L., Qiao J.F. An efficient self-organizing RBF neural network for water quality predicting. Neural Networks 2011, 24(7):717-725.
9. Han M., Fan J.C., Wang J. A dynamic feedforward neural network based on Gaussian particle swarm optimization and its application for predictive control. IEEE Transactions on Neural Networks 2011, 22(9):1457-1468.
10. Han H.G., Qiao J.F. Adaptive dissolved oxygen control based on dynamic structure neural network. Applied Soft Computing 2011, 11(4):3812-3820.
11. Han H.G., Qiao J.F. An adaptive computation algorithm for RBF neural network. IEEE Transactions on Neural Networks and Learning Systems 2012, 23(2):342-347.
12. Hsu C.F. Adaptive growing-and-pruning neural network control for a linear piezoelectric ceramic motor. Engineering Applications of Artificial Intelligence 2008, 21(8):1153-1163.
13. Huang G.B., Chen L. Enhanced random search based incremental extreme learning machine. Neurocomputing 2008, 71(16-18):3460-3468.
14. Huynh T.Q., Reggia J.A. Guiding hidden layer representations for improved rule extraction from neural networks. IEEE Transactions on Neural Networks 2011, 22(2):264-275.
15. Islam M.M., Murase K. A new algorithm to design compact two hidden-layer artificial neural networks. Neural Networks 2001, 14(9):1265-1278.
16. Islam M.M., Sattar M.A., Amin M.F., Yao X., Murase K. A new adaptive merging and growing algorithm for designing artificial neural networks. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 2009, 39(3):705-722.
17. Jing X.J. Robust adaptive learning of feedforward neural networks via LMI optimizations. Neural Networks 2012, 31(1):33-45.
18. Jose G.R., Anastassia A., Juan M.G.C., Alexandra P., Sergio O.E., Vicente M.G. Autonomous growing neural gas for applications with time constraint: optimal parameter estimation. Neural Networks 2012, 32(1):196-208.
19. Kamimura R. Selective information enhancement learning for creating interpretable representations in competitive learning. Neural Networks 2011, 24(4):387-405.
20. Krivov S., Ulanowicz R.E., Dahiya A. Quantitative measures of organization for multiagent systems. BioSystems 2003, 69(1):39-54.
21. Kwok T.Y., Yeung D.Y. Constructive algorithms for structure learning in feedforward neural networks for regression problems. IEEE Transactions on Neural Networks 1997, 8(3):630-645.
22. Lauret P., Fock E., Mara T.A. A node pruning algorithm based on a Fourier amplitude sensitivity test method. IEEE Transactions on Neural Networks 2006, 17(2):273-293.
23. Lehtokangas M. Modelling with constructive backpropagation. Neural Networks 1999, 12(5):707-716.
24. Leng G., McGinnity T.M., Prasad G. Design for self-organizing fuzzy neural networks based on genetic algorithms. IEEE Transactions on Fuzzy Systems 2006, 14(6):755-766.
25. Leung C.S., Wong K.W., Sum P.F., Chan L.W. A pruning method for the recursive least squared algorithm. Neural Networks 2001, 14(2):147-174.
26. Ma L., Khorasani K. Constructive feedforward neural networks using Hermite polynomial activation functions. IEEE Transactions on Neural Networks 2005, 16(4):821-833.
27. Mahmood A., Hamed D., Alireza S., Sylvain C. Feedback associative memory based on a new hybrid model of generalized regression and self-feedback neural networks. Neural Networks 2010, 23(7):892-904.
28. Mantzaris D., Anastassopoulos G., Adamopoulos A. Genetic algorithm pruning of probabilistic neural networks in medical disease estimation. Neural Networks 2011, 24(8):831-835.
29. Minku F.L., Ludermir T.B. Clustering and co-evolution to construct neural network ensembles: an experimental study. Neural Networks 2008, 21(8):1363-1379.
30. Narasimha P.L., Delashmit W.H., Manry M.T., Li J., Maldonado F. An integrated growing-pruning method for feedforward network training. Neurocomputing 2008, 71(13-15):2831-2847.
31. Neves G., Cooke S.F., Bliss T.V.P. Synaptic plasticity, memory and the hippocampus: a neural network approach to causality. Nature Reviews Neuroscience 2008, 9(1):65-75.
32. Nielsen A.B., Hansen L.K. Structure learning by pruning in independent component analysis. Neurocomputing 2008, 71(10-12):2281-2290.
33. Oong T.H., Isa N.A.M. Adaptive evolutionary artificial neural networks for pattern classification. IEEE Transactions on Neural Networks 2011, 22(11):1823-1825.
34. Peng J.X., Li K., Irwin G.W. A new Jacobian matrix for optimal learning of single-layer neural networks. IEEE Transactions on Neural Networks 2008, 19(1):119-129.
35. Qiao J.F., Zhang Y., Han H.G. Fast unit pruning algorithm for feedforward neural network design. Applied Mathematics and Computation 2008, 205(2):622-627.
36. Rabinovich M., Huerta R., Laurent G. Transient dynamics for neural processing. Science 2008, 321(5885):48-50.
37. Ranhel J. Neural assembly computing. IEEE Transactions on Neural Networks and Learning Systems 2012, 23(6):916-927.
38. Ransohoff R.M., Cardona A.E. The myeloid cells of the central nervous system parenchyma. Nature 2010, 468:253-262.
39. Reed R. Pruning algorithms-a survey. IEEE Transactions on Neural Networks 1993, 4(5):740-747.
40. Ren X.M., Lv X.H. Identification of extended Hammerstein systems using dynamic self-optimizing neural networks. IEEE Transactions on Neural Networks 2011, 22(8):1169-1179.
41. Romeroa E., Alquezar R. Comparing error minimized extreme learning machines and support vector sequential feed-forward neural networks. Neural Networks 2012, 25(1):122-129.
42. Sentiono R. Feedforward neural network construction using cross validation. Neural Computation 2001, 13(12):2865-2877.
43. Song Q., Spall J.C., Soh Y.C., Ni J. Robust neural network tracking controller using simultaneous perturbation stochastic approximation. IEEE Transactions on Neural Networks 2008, 19(5):817-835.
44. Tsodyks M., Gilbert C. Neural networks and perceptual learning. Nature 2004, 431:775-781.
45. Wu W., Wang J., Cheng M.S., Li Z.X. Convergence analysis of online gradient method for BP neural networks. Neural Networks 2011, 24(1):91-98.
46. Xin Y., Islam M.M. Evolving artificial neural network ensembles. IEEE Transactions on Computational Intelligence Magazine 2008, 3(1):31-42.
47. Zhang Y.L., Liu R.Y., Heberton G.A., Smolen P., Baxter D.A., Cleary L.J., et al. Computational design of enhanced learning protocols. Nature Neuroscience 2012, 15(2):294-299.
48. Zhang R., Xu Z.B., Huang G.B., Wang D.H. Global convergence of online BP training with dynamic learning rate. IEEE Transactions on Neural Networks and Learning Systems 2012, 23(2):330-341.
49. Zhong S.M., Zeng X.Q., Wu S.L., Han L.X. Sensitivity-based adaptive learning rules for binary feedforward neural networks. IEEE Transactions on Neural Networks and Learning Systems 2012, 23(3):480-491.