Speedup of implementing fuzzy neural networks with high-dimensional inputs through parallel processing on graphic processing units

IEEE Transactions on Fuzzy Systems

Volumn 19, Issue 4, 2011, Pages 717-728

  Juang, Chia Feng ^a   Chen, Teng Chang ^a   Cheng, Wei Yuan ^a  

^a NATIONAL CHUNG HSING UNIVERSITY   (Taiwan)

Author keywords

Classification; compute unified device architecture (CUDA); fuzzy neural networks (FNNs); graphic processing unit (GPU); neural fuzzy systems

Indexed keywords

COMPUTE UNIFIED DEVICE ARCHITECTURES; DATA SETS; FUZZY-NEURAL; GPU IMPLEMENTATION; GRAPHIC PROCESSING UNIT (GPU); GRAPHIC PROCESSING UNITS; HIGH-DIMENSIONAL; INPUT DATAS; INPUT VARIABLES; ITS EFFICIENCIES; LEARNING PERFORMANCE; MEMORY MANAGEMENT; MULTIPLE THREADS; NEURAL FUZZY INFERENCE NETWORK; NEURAL FUZZY SYSTEMS; NUMBER OF THREADS; ON CHIP MEMORY; PARALLEL PROCESSING; PARAMETER LEARNING; SELF-CONSTRUCTING; SINGLE-THREADED; SOFTWARE PLATFORMS; TRAINING TIME;

COMPUTER GRAPHICS EQUIPMENT; DATA PROCESSING; ELECTRONIC EQUIPMENT; FUZZY INFERENCE; FUZZY RULES; IMAGE CODING; NETWORK ARCHITECTURE; PROGRAM PROCESSORS;

FUZZY NEURAL NETWORKS;

EID: 80051493000     PISSN: 10636706     EISSN: None     Source Type: Journal    
DOI: 10.1109/TFUZZ.2011.2140326     Document Type: Article

References (29)

1. J. S. Jang, "ANFIS: Adaptive-network-based fuzzy inference system," IEEE Trans. Syst., Man, Cybern., vol. 23, no. 3, pp. 665-685, May 1993.
2. C. F. Juang and C. T. Lin, "An on-line self-constructing neural fuzzy inference network and its applications," IEEE Trans. Fuzzy Syst., vol. 6, no. 1, pp. 12-32, Feb. 1998.
3. D. Kukolj and E. Levi, "Identification of complex systems based on neural and Takagi-Sugeno fuzzy model," IEEE Trans. Syst., Man, Cybern., B, Cybern., vol. 34, no. 1, pp. 272-282, Feb. 2004.
4. N. K. Kasabov and Q. Song, "DENFIS: Dynamic evolving neural-fuzzy inference system and its application for time-series prediction," IEEE Trans. Fuzzy Syst., vol. 10, no. 2, pp. 144-154, Apr. 2002. (Pubitemid 34554860)
5. P.P. Angelov and D. P. Filev, "An approach to online identification of Takagi-Sugeno fuzzy models," IEEE Trans. Syst., Man Cybern., B, Cybern., vol. 34, no. 1, pp. 484-498, Feb. 2004.
6. P. P. Angelov and D. P. Filev, "Simpl-eTS: A simplified method for learning evolving Takagi-Sugeno fuzzy models," in Proc. Int. Conf. Fuzzy Syst., 2005, pp. 1068-1072.
7. H. J. Rong, N. Sundararajan, G. B. Huang, and P. Saratchandran, "Sequential adaptive fuzzy inference system (SAFIS) for nonlinear system identification and prediction," Fuzzy Sets Syst., vol. 157, no. 9, pp. 1260-1275, 2006.
8. P. Angelov and X. Zhou, "Evolving fuzzy systems from data streams in real-time," in Proc. Symp. Evolving Fuzzy Syst., 2006, pp. 29-35. (Pubitemid 46948085)
9. C. F. Juang and Y. W. Tsao, "A self-evolving interval type-2 fuzzy neural network with on-line structure and parameter learning," IEEE Trans. Fuzzy Syst., vol. 16, no. 6, pp. 1411-1424, Dec. 2008.
10. J. D. Rubio, "SOFMLS: Online self-organizing fuzzy modified leastsquares network," IEEE Trans. Fuzzy Syst., vol. 17, no. 6, pp. 1296-1309, Dec. 2009.
11. J. A. M. HernandezmF.G. Castaneda and J. A. M. Cadenas, "An evolving fuzzy neural network based on the mapping of similarities," IEEE Trans. Fuzzy Syst., vol. 17, no. 6, pp. 1379-1396, Dec. 2009.
12. J. J. Rubio and J. Pacheco, "Astable online clustering fuzzy neural network for nonlinear systems identification," Neural Comput. Appl., vol. 18, no. 6, pp. 633-641, 2009.
13. J. A. Iglesias, P. Angelov, A. Ledezma, and A. Sanchis, "Evolving classification of agents' behavior: A general approach," Evolving Syst., vol. 1, no. 3, pp. 161-171, 2010.
14. J. J. Rubio, D. M. Vázquez, and J. Pacheco, "Backpropagation to train an evolving radial basis function neural network," Evolving Syst., vol. 1, no. 3, pp. 173-180, 2010.
15. J. J. Rubio Avila, "Stability analysis for an online evolving neuro-fuzzy recurrent neural network," in Evolving Intelligent Systems: Methodology and Applications, P. Angelov, D. P. Filev, and N. Kasabov, Eds. New York: Wiley-IEEE Press, 2010, ch. 8, pp. 173-198.
16. J. D. Owens, M. Houston, D. Luebke, S. Green, J. E. Stone, and J. C. Phillips, "GPU computing," Proc. IEEE, vol. 96, no. 5, pp. 879-899, May 2008.
17. J. Nickolls and W. J. Dally, "The GPU computing era," IEEE Micro, vol. 30, no. 2, pp. 56-69, Mar./Apr. 2010.
18. K. S. Kyong and K. Jung, "GPU implementation of neural network," Pattern Recognit., vol. 37, no. 6, pp. 1311-1314, 2004.
19. D. T. Anderson, R. H. Luke, and J. M.Keller, "Speedup of fuzzy clustering through stream processing on graphics processing units," IEEE Trans. Fuzzy Syst., vol. 16, no. 4, pp. 1101-1106, Aug. 2008.
20. Y. Tao, H. Lin, and H. Bao, "GPU-based shooting and bouncing ray method for fast RCS prediction," IEEE Trans. Antennas Propag., vol. 58, no. 2, pp. 494-502, Feb. 2010.
21. M. D. Bisceglie, M. D. Santo, C. Galdi, R. Lanari, and N. Ranaldo, "Synthetic aperture radar processing with GPGPU," IEEE Signal Process. Mag., vol. 27, no. 2, pp. 69-78, Mar. 2010.
22. T. R. Halfhill, "Parallel processing with CUDA-NVIDA's highperformance computing platform uses massive multithreading," Microprocessor Rep., pp. 1-8, Jan. 2008.
23. NVIDIA. CUDA. (2011). [Online]. Available at http://www.nvidia.com/ object/cuda-home-new.html
24. C. T. Lin, C. M. Yeh, S. F. Liang, J. F. Chung, and N. Kumar, "Supportvector-based fuzzy neural network for pattern classification," IEEE Trans. Fuzzy Syst., vol. 14, no. 1, pp. 31-41, Feb. 2006. (Pubitemid 43263926)
25. H. M. Lee, C. M. Chen, J. M. Chen, and Y. L. Jou, "An efficient fuzzy classifier with feature selection based on fuzzy entropy," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 31, no. 3, pp. 426-432, Jun. 2001.
26. D. Michie, D. J. Spiegelhalter, and C. C. Taylor (1994), Machine learning, neural and statistical classification [Online]. Available: ftp.ncc.up.pt/pub/statlog/
27. C.W. Hsu and C. J. Lin, "A comparison of methods for multiclass support vector machines," IEEE Trans. Neural Netw., vol. 13, no. 2, pp. 415-525, Mar. 2002.
28. K. M. Lin and C. J. Lin, "A study on reduced support vector machines," IEEE Trans. Neural Netw., vol. 14, no. 6, pp. 1449-1459, Nov. 2003.
29. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed. Berlin, Germany: Springer-Verlag, 2009, ch. 1.