A multilayer feed forward small-world neural network controller and its application on electrohydraulic actuation system

Journal of Applied Mathematics

Volumn 2013, Issue , 2013, Pages

  Li, Xiaohu ^a   Xu, Feng ^a   Zhang, Jinhua ^a   Wang, Sunan ^a  

^a XI'AN JIAOTONG UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84879368650     PISSN: 1110757X     EISSN: 16870042     Source Type: Journal    
DOI: 10.1155/2013/872790     Document Type: Article

References (34)

1. Batalle D., Eixarch E., Figueras F., Muñoz-Moreno E., Bargallo N., Illa M., Acosta-Rojas R., Amat-Roldan I., Gratacos E., Altered small-world topology of structural brain networks in infants with intrauterine growth restriction and its association with later neurodevelopmental outcome. NeuroImage 2012 60 2 1352 1366 2-s2.0-84856869834 10.1016/j.neuroimage.2012.01. 059
2. Bolaños M., Bernat E. M., He B., Aviyente S., A weighted small world network measure for assessing functional connectivity. Journal of Neuroscience Methods 2013 212 1 133 142
3. Sporns O., Honey C. J., Small worlds inside big brains. Proceedings of the National Academy of Sciences of the United States of America 2006 103 51 19219 19220 2-s2.0-33845909323 10.1073/pnas.0609523103
4. Albert R., Barabási A. L., Statistical mechanics of complex networks. Reviews of Modern Physics 2002 74 1 47 97 10.1103/RevModPhys.74.47 MR1895096 ZBL1205.82086
5. Lu J., Ho D. W. C., Wang Z., Pinning stabilization of linearly coupled stochastic neural networks via minimum number of controllers. IEEE Transactions on Neural Networks 2009 20 10 1617 1629 2-s2.0-70350347096 10.1109/TNN.2009. 2027810
6. Cao J., Li P., Wang W., Global synchronization in arrays of delayed neural networks with constant and delayed coupling. Physics Letters A 2006 353 4 318 325 2-s2.0-33645876794 10.1016/j.physleta.2005.12.092
7. Xia Y., Wang J., A general methodology for designing globally convergent optimization neural networks. IEEE Transactions on Neural Networks 1998 9 6 1331 1343 2-s2.0-0032208805 10.1109/72.728383
8. Hu S., Liao X., Mao X., Stochastic Hopfield neural networks. Journal of Physics A 2003 36 9 2235 2249 10.1088/0305-4470/36/9/303 MR1965155 ZBL1042.82036
9. Stanley K. O., D'Ambrosio D. B., Gauci J., A hypercube-based encoding for evolving large-scale neural networks. Artificial Life 2009 15 2 185 212 2-s2.0-67650188046 10.1162/artl.2009.15.2.15202
10. de Lautour O. R., Omenzetter P., Prediction of seismic-induced structural damage using artificial neural networks. Engineering Structures 2009 31 2 600 606 2-s2.0-57849094451 10.1016/j.engstruct.2008.11.010
11. Gelenbe E., Fourneau J. M., Random neural networks with multiple classes of signals. Neural Computation 1999 11 4 953 963 2-s2.0-0033561945
12. Watts D. J., Strogatz S. H., Collective dynamics of 'small-world9 networks. Nature 1998 393 6684 440 442 2-s2.0-0032482432
13. Pagani G. A., Aiello M., The power grid as a complex network: a survey. Physica A 2013 392 11 2688 2700
14. Gerhard F., Pipa G., Lima B., Neuenschwander S., Gerstner W., Extraction of network topology from multi-electrode recordings: is there a small-world effect? Frontiers in Computational Neuroscience 2011 5 4 1 13 2-s2.0-84855208763 10.3389/fncom.2011.00004
15. Rozenfeld H. D., Song C., Makse H. A., Small-world to fractal transition in complex networks: a renormalization group approach. Physical Review Letters 2010 104 2 2-s2.0-74949109082 10.1103/PhysRevLett.104.025701 025701
16. Backes A. R., Casanova D., Bruno O. M., A complex network-based approach for boundary shape analysis. Pattern Recognition 2009 42 1 54 67 2-s2.0-51649097567 10.1016/j.patcog.2008.07.006
17. Karsai M., Kivelä M., Pan R. K., Kaski K., Kertész J., Barabási A. L., Saramäki J., Small but slow world: how network topology and burstiness slow down spreading. Physical Review E 2011 83 2 2-s2.0-79952504668 10.1103/PhysRevE.83.025102 025102
18. Wu C., Zhou B., Complex network analysis of tag as a social network Journal of Zhejiang University 2010 44 11 2194 2197 2-s2.0-78650766357 10.3785/j.issn.1008-973X.2010.11.027
19. Eguíluz V. M., Chialvo D. R., Cecchi G. A., Baliki M., Apkarian A. V., Scale-free brain functional networks. Physical Review Letters 2005 94 1 2-s2.0-18144406182 10.1103/PhysRevLett.94.018102 018102
20. Bassett D. S., Bullmore E., Small-world brain networks. Neuroscientist 2006 12 6 512 523 2-s2.0-33750466904 10.1177/1073858406293182
21. Piersa J., Piekniewski F., Schreiber T., Theoretical model for mesoscopic-level scale-free self-organization of functional brain networks. IEEE Transactions on Neural Networks 2010 21 11 1747 1758 2-s2.0-78149358665 10.1109/TNN.2010.2066989
22. Li C., Memorizing morph patterns in small-world neuronal network. Physica A 2009 388 2-3 240 246 2-s2.0-55949106677 10.1016/j.physa.2008.10.004
23. Lu S., Fang J., Guo A., Peng Y., Impact of network topology on decision-making. Neural Networks 2009 22 1 30 40 2-s2.0-58249083748 10.1016/j.neunet.2008.09.012
24. Erkaymaz O., Özer M., Yumuşakc N., Performance analysis of a feed forward artifical neural network with small-world topology. Procedia Technology 2012 1 291 296 10.1016/j.protcy.2012.02.062
25. Simard D., Nadeau L., Kröger H., Fastest learning in small-world neural networks. Physics Letters A 2005 336 1 8 15 2-s2.0-13144282731 10.1016/j.physleta.2004.12.078
26. Trtnik G., Kavčič F., Turk G., Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networks. Ultrasonics 2009 49 1 53 60 2-s2.0-57049177642 10.1016/j.ultras.2008.05.001
27. Kůrková V., Kainen P. C., Kreinovich V., Estimates of the number of hidden units and variation with respect to half-spaces. Neural Networks 1997 10 6 1061 1068 2-s2.0-0343118761 10.1016/S0893-6080(97)00028-2
28. Yuan Z. Z., Chen Z. Q., Li X., Connectionism intelligent control: a survey Acta Automatica Sinica 2002 28 1 38 59
29. Ugalde H. M. R., Carmona J. C., Alvarado V. M., Reyes-Reyes J., Neural network design and model reduction approach for black box nonlinear system identification with reduced number of parameters. Neurocomputing 2013 101 170 180
30. Habibi S., Burton R., Sampson E., High precision hydrostatic actuation systems for micro- and nanomanipulation of heavy loads. Transactions of the ASME 2006 128 4 778 787 2-s2.0-33845994753 10.1115/1.2229259
31. Habibi S. R., Burton R., Parameter identification for a high-performance hydrostatic actuation system using the variable structure filter concept. Transactions of the ASME 2007 129 2 229 235 2-s2.0-34248173054 10.1115/1.2431816
32. Liu J. L., Jiang J. H., Lu T. R., Liu Q. H., Zhang C. W., Xu H. G., Direct drive volume control electro-hydraulic servo active mass driver system Journal of Harbin Institute of Technology 2011 43 9 51 55 2-s2.0-80655149231
33. Elbayomy K. M., Jiao Z., Zhang H., PID controller optimization by GA and its performances on the electro-hydraulic servo control system. Chinese Journal of Aeronautics 2008 21 4 378 384 2-s2.0-50849135863 10.1016/S1000-9361(08)60049- 7
34. Yao J., Jiao Z., Shang Y., Huang C., Adaptive nonlinear optimal compensation control for electro-hydraulic load simulator. Chinese Journal of Aeronautics 2010 23 6 720 733 2-s2.0-78650819593 10.1016/S1000-9361(09)60275-2