Self-adaptive extreme learning machine

Neural Computing and Applications

Volumn 27, Issue 2, 2016, Pages 291-303

  Wang, Gai Ge ^a,b   Lu, Mei ^a   Dong, Yong Quan ^a   Zhao, Xiang Jun ^a  

^a JIANGSU NORMAL UNIVERSITY   (China)

^b NORTHEAST NORMAL UNIVERSITY   (China)

Author keywords

Back propagation; Classification; Extreme learning machine; General regression neural network; Self adaptive

Indexed keywords

ALGORITHMS; BACKPROPAGATION; CLASSIFICATION (OF INFORMATION); FEEDFORWARD NEURAL NETWORKS; KNOWLEDGE ACQUISITION; LEARNING ALGORITHMS; NETWORK LAYERS; NEURAL NETWORKS;

EXTREME LEARNING MACHINE; GENERAL REGRESSION NEURAL NETWORK; GENERALIZATION PERFORMANCE; IRIS CLASSIFICATIONS; SELF-ADAPTIVE; SELF-ADAPTIVE LEARNING ALGORITHMS; SELF-ADAPTIVE MECHANISMS; SINGLE-HIDDEN LAYER FEEDFORWARD NEURAL NETWORKS;

LEARNING SYSTEMS;

EID: 84955730358     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-015-1874-3     Document Type: Article

References (57)

1. Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501. doi:10.1016/j.neucom.2005.12.126
2. Huang G-B, Chen L (2007) Convex incremental extreme learning machine. Neurocomputing 70(16–18):3056–3062. doi:10.1016/j.neucom.2007.02.009
3. Huang G-B, Wang DH, Lan Y (2011) Extreme learning machines: a survey. Int J Mach Learn Cyber 2(2):107–122. doi:10.1007/s13042-011-0019-y
4. Horata P, Chiewchanwattana S, Sunat K (2013) Robust extreme learning machine. Neurocomputing 102:31–44. doi:10.1016/j.neucom.2011.12.045
5. Yang Y, Wang Y, Yuan X (2012) Bidirectional extreme learning machine for regression problem and its learning effectiveness. IEEE Trans Neural Netw Learn Syst 23(9):1498–1505. doi:10.1109/TNNLS.2012.2202289
6. Jun W, Shitong W, Chung F (2011) Positive and negative fuzzy rule system, extreme learning machine and image classification. Int J Mach Learn Cyber 2(4):261–271. doi:10.1007/s13042-011-0024-1
7. Pouzols FM, Lendasse A (2010) Evolving fuzzy optimally pruned extreme learning machine for regression problems. Evol Syst 1(1):43–58. doi:10.1007/s12530-010-9005-y
8. Li G, Niu P (2011) An enhanced extreme learning machine based on ridge regression for regression. Neural Comput Appl 22(3–4):803–810. doi:10.1007/s00521-011-0771-7
9. Zhai J-H, Xu H-Y, Wang X-Z (2012) Dynamic ensemble extreme learning machine based on sample entropy. Soft Comput 16(9):1493–1502. doi:10.1007/s00500-012-0824-6
10. Zong W, Huang G-B, Chen Y (2013) Weighted extreme learning machine for imbalance learning. Neurocomputing 101:229–242. doi:10.1016/j.neucom.2012.08.010
11. He Q, Shang T, Zhuang F, Shi Z (2013) Parallel extreme learning machine for regression based on MapReduce. Neurocomputing 102:52–58. doi:10.1016/j.neucom.2012.01.040
12. Wang D, Alhamdoosh M (2013) Evolutionary extreme learning machine ensembles with size control. Neurocomputing 102:98–110. doi:10.1016/j.neucom.2011.12.046
13. Feng G, Huang G-B, Lin Q, Gay R (2009) Error minimized extreme learning machine with growth of hidden nodes and incremental learning. IEEE Trans Neural Netw 20(8):1352–1357. doi:10.1109/TNN.2009.2024147
14. Wang L, Huang Y, Luo X, Wang Z, Luo S (2011) Image deblurring with filters learned by extreme learning machine. Neurocomputing 74(16):2464–2474. doi:10.1016/j.neucom.2010.12.035
15. Iosifidis A, Tefas A, Pitas I (2013) Dynamic action recognition based on dynemes and extreme learning machine. Pattern Recogn Lett 34(15):1890–1898. doi:10.1016/j.patrec.2012.10.019
16. Song Y, Crowcroft J, Zhang J (2012) Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine. J Neurosci Methods 210(2):132–146. doi:10.1016/j.jneumeth.2012.07.003
17. Chacko BP, Vimal Krishnan VR, Raju G, Babu Anto P (2011) Handwritten character recognition using wavelet energy and extreme learning machine. Int J Mach Learn Cyber 3(2):149–161. doi:10.1007/s13042-011-0049-5
18. Zheng W, Qian Y, Lu H (2012) Text categorization based on regularization extreme learning machine. Neural Comput Appl 22(3–4):447–456. doi:10.1007/s00521-011-0808-y
19. Hu X-F, Zhao Z, Wang S, Wang F-L, He D-K, Wu S-K (2007) Multi-stage extreme learning machine for fault diagnosis on hydraulic tube tester. Neural Comput Appl 17(4):399–403. doi:10.1007/s00521-007-0139-1
20. Suresh S, Venkatesh Babu R, Kim HJ (2009) No-reference image quality assessment using modified extreme learning machine classifier. Appl Soft Compt 9(2):541–552. doi:10.1016/j.asoc.2008.07.005
21. Chen FL, Ou TY (2011) Sales forecasting system based on Gray extreme learning machine with Taguchi method in retail industry. Expert Syst Appl 38(3):1336–1345. doi:10.1016/j.eswa.2010.07.014
22. Malathi V, Marimuthu NS, Baskar S, Ramar K (2011) Application of extreme learning machine for series compensated transmission line protection. Eng Appl Artif Intel 24(5):880–887. doi:10.1016/j.engappai.2011.03.003
23. Zhao X (2010) A perturbed particle swarm algorithm for numerical optimization. Appl Soft Compt 10(1):119–124. doi:10.1016/j.asoc.2009.06.010
24. Zhao X, Liu Z, Yang X (2014) A multi-swarm cooperative multistage perturbation guiding particle swarm optimizer. Appl Soft Compt 22:77–93. doi:10.1016/j.asoc.2014.04.042
25. Li X, Yin M (2012) Application of differential evolution algorithm on self-potential data. PLoS One 7(12):e51199. doi:10.1371/journal.pone.0051199
26. Zou D, Liu H, Gao L, Li S (2011) An improved differential evolution algorithm for the task assignment problem. Eng Appl Artif Intel 24(4):616–624. doi:10.1016/j.engappai.2010.12.002
27. Li X, Yin M (2014) Parameter estimation for chaotic systems by hybrid differential evolution algorithm and artificial bee colony algorithm. Nonlinear Dynam 77(1–2):61–71. doi:10.1007/s11071-014-1273-9
28. Zhu QY, Qin AK, Suganthan PN, Huang GB (2005) Evolutionary extreme learning machine. Pattern Recogn 38(10):1759–1763. doi:10.1016/j.patcog.2005.03.028
29. Cao J, Lin Z, Huang G-B (2012) Self-adaptive evolutionary extreme learning machine. Neural Process Lett 36(3):285–305. doi:10.1007/s11063-012-9236-y
30. Gandomi AH, Yang X-S, Alavi AH (2013) Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng Comput 29(1):17–35. doi:10.1007/s00366-011-0241-y
31. Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: Abraham A, Carvalho A, Herrera F, Pai V (eds) Proceeding of world congress on nature & biologically inspired computing (NaBIC 2009), Coimbatore, India, December 2009. IEEE Publications, USA, pp 210–214
32. Wang G-G, Guo L, Duan H, Wang H (2014) A new improved firefly algorithm for global numerical optimization. J Comput Theor Nanosci 11(2):477–485. doi:10.1166/jctn.2014.3383
33. Yang XS (2010) Firefly algorithm, stochastic test functions and design optimisation. Int J Bio-Inspired Comput 2(2):78–84. doi:10.1504/IJBIC.2010.032124
34. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61. doi:10.1016/j.advengsoft.2013.12.007
35. Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68. doi:10.1177/003754970107600201
36. Wang G, Guo L, Wang H, Duan H, Liu L, Li J (2014) Incorporating mutation scheme into krill herd algorithm for global numerical optimization. Neural Comput Appl 24(3–4):853–871. doi:10.1007/s00521-012-1304-8
37. Zou D, Gao L, Li S, Wu J (2011) Solving 0–1 knapsack problem by a novel global harmony search algorithm. Appl Soft Compt 11(2):1556–1564. doi:10.1016/j.asoc.2010.07.019
38. Wang G, Guo L, Duan H, Wang H, Liu L, Shao M (2013) Hybridizing harmony search with biogeography based optimization for global numerical optimization. J Comput Theor Nanosci 10(10):2318–2328. doi:10.1166/jctn.2013.3207
39. Simon D (2008) Biogeography-based optimization. IEEE Trans Evolut Comput 12(6):702–713. doi:10.1109/TEVC.2008.919004
40. Wang G-G, Gandomi AH, Alavi AH (2014) An effective krill herd algorithm with migration operator in biogeography-based optimization. Appl Math Model 38(9–10):2454–2462. doi:10.1016/j.apm.2013.10.052
41. Saremi S, Mirjalili S, Lewis A (2014) Biogeography-based optimisation with chaos. Neural Comput Appl 25(5):1077–1097. doi:10.1007/s00521-014-1597-x
42. Li X, Yin M (2013) Multiobjective binary biogeography based optimization for feature selection using gene expression data. IEEE Trans Nanobiosci 12(4):343–353. doi:10.1109/TNB.2013.2294716
43. Li X, Zhang J, Yin M (2014) Animal migration optimization: an optimization algorithm inspired by animal migration behavior. Neural Comput Appl 24(7–8):1867–1877. doi:10.1007/s00521-013-1433-8
44. Mirjalili S, Wang G-G, Coelho LdS (2014) Binary optimization using hybrid particle swarm optimization and gravitational search algorithm. Neural Comput Appl 25(6):1423–1435. doi:10.1007/s00521-014-1629-6
45. Mirjalili S, Lewis A (2014) Adaptive gbest-guided gravitational search algorithm. Neural Comput Appl 25(7–8):1569–1584. doi:10.1007/s00521-014-1640-y
46. Mirjalili S, Mohd Hashim SZ, Moradian Sardroudi H (2012) Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm. Appl Math Comput 218(22):11125–11137. doi:10.1016/j.amc.2012.04.069
47. Zhang Z, Zhang N, Feng Z (2014) Multi-satellite control resource scheduling based on ant colony optimization. Expert Syst Appl 41(6):2816–2823. doi:10.1016/j.eswa.2013.10.014
48. Zhang Z, Feng Z (2012) Two-stage updating pheromone for invariant ant colony optimization algorithm. Expert Syst Appl 39(1):706–712. doi:10.1016/j.eswa.2011.07.062
49. Gandomi AH (2014) Interior search algorithm (ISA): a novel approach for global optimization. ISA Trans 53(4):1168–1183. doi:10.1016/j.isatra.2014.03.018
50. Gandomi AH, Yang X-S, Alavi AH, Talatahari S (2013) Bat algorithm for constrained optimization tasks. Neural Comput Appl 22(6):1239–1255. doi:10.1007/s00521-012-1028-9
51. Yang XS, Gandomi AH (2012) Bat algorithm: a novel approach for global engineering optimization. Eng Comput 29(5):464–483. doi:10.1108/02644401211235834
52. Gandomi AH, Alavi AH (2012) Krill herd: a new bio-inspired optimization algorithm. Commun Nonlinear Sci Numer Simul 17(12):4831–4845. doi:10.1016/j.cnsns.2012.05.010
53. Wang G-G, Gandomi AH, Alavi AH (2014) Stud krill herd algorithm. Neurocomputing 128:363–370. doi:10.1016/j.neucom.2013.08.031
54. Guo L, Wang G-G, Gandomi AH, Alavi AH, Duan H (2014) A new improved krill herd algorithm for global numerical optimization. Neurocomputing 138:392–402. doi:10.1016/j.neucom.2014.01.023
55. Wang G-G, Gandomi AH, Alavi AH (2013) A chaotic particle-swarm krill herd algorithm for global numerical optimization. Kybernetes 42(6):962–978. doi:10.1108/K-11-2012-0108
56. Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 1(1):3–18. doi:10.1016/j.swevo.2011.02.002
57. Wang G-G, Guo L, Gandomi AH, Hao G-S, Wang H (2014) Chaotic krill herd algorithm. Inf Sci 274:17–34. doi:10.1016/j.ins.2014.02.123