Performance of the IDS method as a soft computing tool

IEEE Transactions on Fuzzy Systems

Volumn 16, Issue 6, 2008, Pages 1582-1596

  Murakami, Masayuki ^a,b   Honda, Nakaji ^a  

^a UNIVERSITY OF ELECTRO COMMUNICATIONS   (Japan)

^b TOSHIBA CORPORATION   (Japan)

Author keywords

Fault tolerance; Ink drop spread (IDS) method; Interpretability; Learning speed; Noise tolerance; Transparency

Indexed keywords

DROPS; ERRORS; FAULT TOLERANT COMPUTER SYSTEMS; FUNCTIONS; FUZZY INFERENCE; FUZZY NEURAL NETWORKS; INK; NEURAL NETWORKS; OPTICAL PROPERTIES; QUALITY ASSURANCE; RELIABILITY; SOFT COMPUTING; SPEED; TRANSPARENCY;

ARTIFICIAL NEURAL NETWORKS; COMPARATIVE EVALUATIONS; COMPUTING TOOLS; FUZZY INFERENCE SYSTEMS; IDS METHODS; IMAGE INFORMATIONS; INK DROP SPREAD (IDS) METHOD; INTERPRETABILITY; LEARNING SPEED; MATHEMATICAL COMPUTATIONS; MODELING PROCESSES; MODELING TECHNIQUES; NEW APPROACHES; NOISE TOLERANCE; PERFORMANCE FACTORS; PRACTICAL COMPUTING;

FAULT TOLERANCE;

EID: 58149527527     PISSN: 10636706     EISSN: None     Source Type: Journal    
DOI: 10.1109/TFUZZ.2008.2005693     Document Type: Article

References (30)

1. L. A. Zadeh, "Soft computing and fuzzy logic," IEEE Softw., vol. 11, no. 6, pp. 48-56, Nov. 1994.
2. S. B. Shouraki, "A novel fuzzy approach to modeling and control and its hardware implementation based on brain functionality and specifications," Ph.D. dissertation, Univ. Electro-Commun., Tokyo, Japan, 2000.
3. S. B. Shouraki and N. Honda, "Recursive fuzzy modeling based on fuzzy interpolation," J. Adv. Comput. Intell. Intell. Informat., vol. 3, no. 2, pp. 114-125, Apr. 1999.
4. M. Murakami and N. Honda, "A study on the modeling ability of the IDS method: A soft computing technique using pattern-based information processing," Int. J. Approx. Reason., vol. 45, no. 3, pp. 470-487, Aug. 2007.
5. J.-N. Hwang, S.-R. Lay, M. Maechler, R. D.Martin, and J. Schimert, "Regression modeling in back-propagation and projection pursuit learning," IEEE Trans. Neural Netw., vol. 5, no. 3, pp. 342-353, May 1994.
6. T.-Y. Kwok and D.-Y. Yeung, "Use of bias term in projection pursuit learning improves approximation and convergence properties," IEEE Trans. Neural Netw., vol. 7, no. 5, pp. 1168-1183, Sep. 1996.
7. T.-Y. Kwok and D.-Y. Yeung, "Objective functions for training new hidden units in constructive neural networks," IEEE Trans. Neural Netw., vol. 8, no. 5, pp. 1131-1148, Sep. 1997.
8. J.-C. Duan and F.-L. Chung, "Cascaded fuzzy neural network model based on syllogistic fuzzy reasoning," IEEE Trans. Fuzzy Syst., vol. 9, no. 2, pp. 293-306, Apr. 2001.
9. B. E. Segee and M. J. Carter, "Comparative fault tolerance of parallel distributed processing networks," IEEE Trans. Comput., vol. 43, no. 11, pp. 1323-1329, Nov. 1994.
10. J.-S. R. Jang, C.-T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence Upper Saddle River, NJ: Prentice-Hall, 1997.
11. L. Ma and K. Khorasani, "Constructive feedforward neural networks using Hermite polynomial activation functions," IEEE Trans. Neural Netw. vol. 16, no. 4, pp. 821-833, Jul. 2005.
12. N. K. Treadgold and T. D. Gedeon, "Exploring constructive cascade networks," IEEE Trans. Neural Netw., vol. 10, no. 6, pp. 1335-1350, Nov. 1999.
13. J.-S. R. Jang, "ANFIS: Adaptive-network-based fuzzy inference system," IEEE Trans. Syst., Man, Cybern., vol. 23, no. 3, pp. 665-685, May/Jun. 1993.
14. J. Gray and D. P. Siewiorek, "High-availability computer systems," IEEE Comput., vol. 24, no. 9, pp. 39-48, Sep. 1991.
15. C. H. Sequin and R. D. Clay, "Fault tolerance in artificial neural networks," in Proc. 1990 Int. Joint Conf. Neural Netw. (IJCNN'90 San Diego, CA, Jun., pp. 703-708.
16. Y. Tan and T. Nanya, "Fault-tolerant back-propagationmodel and its generalization ability," in Proc. 1993 Int. Joint Conf. Neural Netw. (IJCNN'93), Nagoya, Japan, Oct., pp. 2516-2519.
17. N. C. Hammadi and H. Ito, "A learning algorithm for fault tolerant feedforward neural networks," IEICE Trans. Inf. Syst., vol. E80-D, no. 1, pp. 21-27, Jan. 1997.
18. D. Deodhare, M. Vidyasagar, and S. S. Keerthi, "Synthesis of fault-tolerant feedforward neural networks using minimax optimization," IEEE Trans. Neural Netw., vol. 9, no. 5, pp. 891-900, Sep. 1998.
19. X. Parra and A. Catala, "Learning fault tolerance in radial basis function networks," in Proc. 9th Eur. Symp. Artif. Neural Netw. (ESANN 2001), Bruges, Belgium, Apr., pp. 341-346.
20. D. S. Phatak and I. Koren, "Complete and partial fault tolerance of feedforward neural nets," IEEE Trans. Neural Netw., vol. 6, no. 2, pp. 446-456, Mar. 1995.
21. J. Conradt, G. Tevatia, S. Vijayakumar, and S. Schaal, "On-line learning for humanoid robot systems," in Proc. 17th Int. Conf. Mach. Learning (ICML 2000), Stanford, CA, Jun., pp. 191-198.
22. Q. Cao, M. H. Lim, J. H. Li, Y. S. Ong, and W. L. Ng, "A context switchable fuzzy inference chip," IEEE Trans. Fuzzy Syst., vol. 14, no. 4, pp. 552-567, Aug. 2006.
23. G.-B. Huang, Q.-Y. Zhu, and C. K. Siew, "Real-time learning capability of neural networks," IEEE Trans. Neural Netw., vol. 17, no. 4, pp. 863-878, Jul. 2006.
24. T. Heskes and W. Wiegerinck, "A theoretical comparison of batch-mode, on-line, cyclic, and almost-cyclic learning," IEEE Trans. Neural Netw., vol. 7, no. 4, pp. 919-925, Jul. 1996.
25. N. Murata and S. Amari, "Statistical analysis of learning dynamics," Signal Process., vol. 74, no. 1, pp. 3-28, Apr. 1999.
26. M. Bianchini, P. Frasconi, and M. Gori, "Learning without local minima in radial basis function networks," IEEE Trans. Neural Netw., vol. 6, no. 3, pp. 749-756, May 1995.
27. S. Mitra and Y. Hayashi, "Neuro-fuzzy rule generation: Survey in soft computing framework," IEEE Trans. Neural Netw., vol. 11, no. 3, pp. 748-768, May 2000.
28. A. Riid, "Transparent fuzzy systems: Modeling and control," Ph.D. dissertation, Tallinn Tech. Univ., Tallinn, Estonia, 2002.
29. M. Setnes, R. Babuska, U. Kaymak, and H. R. van Nauta Lemke, "Similarity measure in fuzzy rule base simplification," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 28, no. 3, pp. 376-386, Jun. 1998.
30. M. Murakami and N. Honda, "A fast structural optimization technique for IDS modeling," in Proc. 2007 Int. Conf. NAFIPS, San Diego, CA, Jun., pp. 204-209.