A novel self-adaptive extreme learning machine based on affinity propagation for radial basis function neural network

Neural Computing and Applications

Volumn 24, Issue 7-8, 2014, Pages 1487-1495

  Ding, Shifei ^a,b   Ma, Gang ^a,b   Shi, Zhongzhi ^b  

^a CHINA UNIVERSITY OF MINING AND TECHNOLOGY   (China)

^b INSTITUTE OF COMPUTING TECHNOLOGY   (China)

Author keywords

Affinity propagation (AP); Extreme learning machine (ELM); Radial basis function neural network (RBFNN)

Indexed keywords

ALGORITHMS; KNOWLEDGE ACQUISITION; OPTIMIZATION;

AFFINITY PROPAGATION; CLUSTERING CENTERS; COEFFICIENT MATRIX; EXTREME LEARNING MACHINE; GENERALIZATION CAPABILITY; HIDDEN LAYER NEURONS; OPTIMAL PARAMETER; RADIAL BASIS FUNCTION NEURAL NETWORKS;

LEARNING SYSTEMS;

EID: 84900839962     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-013-1385-z     Document Type: Article

References (21)

1. Muzhou Hou, Xuli Han (2011) The multidimensional function approximation based on constructive wavelet RBF neural network. Appl Soft Comput 11(2): 2173-2177.
2. Beyhan S, Alci M (2010) Stable modeling based control methods using a new RBF network. ISA Trans 49(4): 510-518.
3. Han H-G, Chen Q, Qiao J-F (2011) An efficient self-organizing RBF neural network for water quality prediction. Neural Netw 24(7): 717-725.
4. Zhang A, Zhang L (2004) RBF neural networks for the prediction of building interference effects. Comput Struct 82(27): 2333-2339.
5. Zeng W, Wang C (2012) Human gait recognition via deterministic learning. Neural Netw 35: 92-102.
6. Shifei D, Ma G, Xu X (2011) A rough RBF neural networks optimized by the genetic algorithm. Adv Inf Sci Serv Sci 3(7): 332-339.
7. Mao Z, Jia M (2012) ELM based LF temperature prediction model and its online sequential learning. Control and Decision Conference (CCDC), 2012 24th Chinese, vol 5. pp 2362-2365.
8. Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1-3): 489-501.
9. Liu X, Gao C, Li P (2012) A comparative analysis of support vector machines and extreme learning machines. Neural Netw 33: 58-66.
10. Park H-S, Chung Y-D, Oh S-K, Pedrycz W, Kim H-K (2011) Design of information granule-oriented RBF neural networks and its application to power supply for high-field magnet. Eng Appl Artif Intell 24(3): 543-554.
11. Shen W, Guo X, Wu C, Wu D (2011) Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm. Knowl Based Syst 24(3): 378-385.
12. Wu X, Zhu X, Cao G, Tu H (2008) Predictive control of SOFC based on a GA-RBF neural network model. J Power Sources 179(1): 232-239.
13. Qu N, Mi H, Wang B, Ren Y (2009) Application of GA-RBF networks to the nondestructive determination of active component in pharmaceutical powder by NIR spectroscopy. J Taiwan Inst Chem Eng 40(2): 163-167.
14. dos Coelho LS, Santos AAP (2011) A RBF neural network model with GARCH errors: application to electricity price forecasting. Electr Power Syst Res 81(1): 74-83.
15. Frey BJ, Dueck D (2007) Clustering by passing messages between data points. Science 315(5814): 972-976.
16. Zhong Y, Zheng M, Wu J, Shen W, Zhou Y, Zhou C (2012) Search the optimal preference of affinity propagation algorithm. In: Intelligent computation technology and automation (ICICTA), 2012 fifth international conference, vol 1. pp 304-307.
17. Cheng L, Hu J (2009) Matrix theory. China University of Mining and Technology Press, Xuzhou.
18. Huang G, Siew C-K (2004) Extreme learning machine: RBF network case. Control Autom Robot Vis Conf 2: 1029-1036.
19. Huang G-B, Zhu Q-Y, Siew C-K (2004) Extreme learning machine: a new learning scheme of feed forward neural networks. In: Proceedings of international joint conference on neural networks (IJCNN2004), vol 6. pp 25-29.
20. Huang G-B, Babri HA (1998) Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions. IEEE Trans Neural Networks 9(1): 224-229.
21. Huang G-B (2003) Learning capability and storage capacity of two-hidden-layer feedforward networks. IEEE Trans Neural Networks 14(2): 274-281.