Synergistic modeling and applications of hierarchical fuzzy neural networks

Proceedings of the IEEE

Volumn 87, Issue 9, 1999, Pages 1550-1574

  Kung, Sun Yuan ^a   Taur, Jinshiuh ^b   Lin, Shang Hung ^c  

^a Princeton University   (United States)

^b NATIONAL CHUNG HSING UNIVERSITY   (Taiwan)

^c Epson Palo Alto Laboratory   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; FUZZY SETS; LEARNING SYSTEMS;

HIERARCHICAL FUZZY NEURAL NETWORKS (FNN);

FEEDFORWARD NEURAL NETWORKS;

EID: 0033364901     PISSN: 00189219     EISSN: None     Source Type: Journal    
DOI: 10.1109/5.784235     Document Type: Article

References (63)

1. S. Abe and R. Thawonmas, "A fuzzy classifier with ellipsoidal regions," IEEE Trans. Fuzzy Syst., vol. 5, pp. 358-368, Aug. 1997.
2. M. A. Abidi and R. C. Gonzalez, Data Fusion in Robotics and Machine Intelligence. Boston, MA: Academic, 1992.
3. J. S. Bridle, "Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition," in Neuro-Compiiting: Algorithms, Architectures, and Applications, F. Fogelman and J. Hérault, Eds. London, U.K.: Springer-Verlag, 1991, pp. 227-236.
4. R. Brunelli and T. Poggio, "Face recognition: Features versus templates," IEEE Trans. Pattern Anal. Machine Intcll., vol. 15, pp. 1042-1052, 1993.
5. Y. Chan, S.-H. Lin, and S. Y. Kung, "Video indexing and retrieval," in Multimedia Technology for Applications, B. Sheu and M. Ismail, Eds. Piscataway, NJ: IEEE Press, 1998, pp. 253-281.
6. Y. Chan, S. H. Lin, Y. P. Tan, and S. Y. Kung, "Video shot classification using human faces," in Proc. IEEE Int. Conf. Image Processing, Lausanne, Switzerland, 1996, pp. 843-846.
7. I. J. Cox, J. Ghosn, and P. Yianilos, "Feature-based face recognition using mixture distance," NEC Res. Inst., Princeton, NJ, Tech. Rep. 95-09, 1995.
8. G. Cybenko, "Approximation by of a sigmoidal function," Math. Control, Signals Syst., vol. 2, pp. 303-314, 1998.
9. Y. Delignon, A. Marzouki, and W. Pieczynski, "Estimation of generalized mixtures and its application in image segmentation," IEEE Trans. Image Processing, vol. 6, pp. 1364-1375, Oct. 1997.
10. A. Dempster, N. Laird, and D. Rubin, "Maximum likelihood from incomplete data via the EM algorithm," J. Roy. Statistical Soc., B, vol. 39, pp. 1-38, 1976.
11. D. Driankov, H. Hellendoom, and M. Reinfrank, An Introduction to Fuzzy Control. Berlin, Germany: Springer-Verlae, 1993.
12. R. O. Duda and P. E. Hart, Pattern Classification and Scene Anialysis. New York: Wiley, 1973.
13. S.-H. Lai and M. Fang, "Robust and automatic adjustment of display window width and center for MR images," in Proc. SPIESymp. Medical Imaging: Image Perception, vol. 3340, San Diego, CA, Feb. 1998, pp. 105-116.
14. _, "A neural network based system for optimal medical image visualization with fast online adaptation capabilities," in Proc. 32nd Annii. Conf. Information Sciences and Systems, Princeton, NJ, Mar. 1998, pp. 715-720.
15. R. Fisher, "The use of multiple measurements in taxonomic problems," Anna. Eugenics, vol. 7, pt. II, pp. 179-188, 1936.
16. K. Funahashi, "On the approximate realization of continuous mappings by Neural Networks," Neural Nenvorks, vol. 2, pp. 183-192, 1989.
17. F. Goudail, E. Lange, T. Ivvamoto, K. Kyuma, and N. Otsu, "Face recognition system using local autocorrelations and multiscale integration," IEEE Trans. Pattern Anal. Machine Intell., vol. 18, Oct. 1996.
18. R. M. Gray, "Vector quantization," IEEE ASSP Mag., vol. 1, pp. 4-29, Apr. 1984.
19. R. J. Hathaway, "Another interpretation of the EM algorithm for mixture distributions," Statist. Prob. Lett., vol. 4, pp. 53-56, 1986.
20. S. Haykin, Neural Nehvorks: A Comprehensive Foundation. New York: Macmillan, 1994.
21. J. N. Hwang and E. Lin, "Mixture of discriminative learning experts of constant sensitivity for automated cytology screening," in Proc. 1997 IEEE Workshop Neural Nerirorksfor Signal Processing, Amelia Island, FL, Sept. 1997, pp. 152-161.
22. R. A. Jacobs, M. I. Jordan, and A. G. Barto, "Task decomposition through competition in a modular connectionist architecture: The what and where vision tasks," Cognitive Sei., vol. 15, pp. 219-250, 1991.
23. R. A. Jacobs, M. I. Jordan, S. J. Nowlan, and G. E. Hinton, "Adaptive mixtures of local experts," Neural Computation, vol. 3, pp. 79-87, 1991.
24. J.-S. R. Jang, C.-T. Sun, and E. Mizutani, Neuro-Fiiny and Soft Computing. Englewood Cliffs, NJ: Prentice-Hall, 1997.
25. M. I. Jordan and R. A. Jacobs, "Hierarchies of adaptive experts," in Kass Advances in Neural Information Systems, vol. 4. San Mateo, CA: Morgan Kaufmann, 1992, pp. 985-992.
26. _, "Hierarchical mixtures of experts and the EM algorithm," Neural Computation, vol. 6, pp. 181-214, 1994.
27. B. H. Juang, S. Y. Kung, and C. A. Kamm, Eds., Proc. IEEE Workshop Neural Net\-orks for Signal Processing, vols. I-VII. Piscataway, NJ: IEEE Press, 1991-1997.
28. B. H. Juang and S. Katagiri, "Discriminative learning for minimum error classification," IEEE Trans. Signal Processing, vol. 40, pp. 3043-3054, Dec. 1992.
29. B. Kosko, "Fuzzy systems are universal approximators," in Proc. IEEE Int. Conf. Fuzzy Systems, San Diego, CA, Mar. 1992, pp. 1153-1162.
30. S. Y. Kung, Digital Neural Networks. Englewood Cliffs, NJ: Prentice-Hall, 1993.
31. S. Y. Kung and J. S. Taur, "Decision-based neural networks with signal/image classification applications," IEEE Trans. Neural Netn-orks, vol. 6, pp. 170-181, Jan. 1995.
32. C. Lau, T. Adah, and Y. Wang, "Co-registration of PET/MR brain images by multi-feature correlation matching," in Proc. 15th Southern Biomédical Engineering Conf., Dayton, OH, Mar. 1996, pp. 301-304.
33. S. Lawrence, C. L. Giles, A. C. Tsoi, and A. D. Back, "Face recognition: A convolutional neural network approach," IEEE Trans. Neural NeMorks, vol. 8, pp. 98-113, Jan. 1997.
34. H. Li, Y. Wang, K.-J. R. Liu, S.-H. B. Lo, and M. T. Freedman, "Statistical model supported approach to radiographie mass detection; Part I-Improving lesion characterization by morphological filtering and site segmentation; Part II-Decision making through minimax entropy modeling and modular neural networks," IEEE Trans. Med. Imaging, to be published.
35. C. T. Lin and C. S. Lee, Neural Fu::y Systems: A NenroFu:zy Synergist to Intelligent systems. Englewood Cliffs, NJ: Prentice-Hall, 1996.
36. S. H. Lin, "Biometrie identification for network security and access control," Ph.D. dissertation, Dep. Elect. Eng., Princeton Univ., Princeton, NJ, 1996.
37. S.-H. Lin, Y. Chan, and S. Y. Kung, "A probabilistic decisionbased neural network for location deformable objects and its applications to surveillance system and video browsing," in Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing, Atlanta, GA, 1996, pp. 3554-3557.
38. S. H. Lin, S. Y. Kung, and L. J. Lin, "Face recognition/detection by probabilistic decision-based neural networks," IEEE Trans. Neural Nertvorks (Special Issue on Artificial Neural Nertvork and Pattern Recognition), vol. 8, pp. 114-132, Jan. 1997.
39. Y. Lin, "Object oriented scene segmentation from visual motion," Ph.D. dissertation, Dep. Elect. Eng., Princeton Univ., Princeton, NJ, 1998.
40. Y. Lin, Y.-K. Chen, and S. Y. Kung, "A principal component clustering approach to object-oriented motion segmentation and estimation," J. VLSI Signal Processing Syst., vol. 17, pp. 163-188, Nov. 1997.
41. M. MacQueen, "Some methods for classification and analysis of multivariate observation," in Proc. 5lh Berkeley Symp. Mathematical Statistics and Probabilities, vol. 1, L. M. LeCun and J. Neyman, Eds. Berkeley, CA: Univ. California Press, 1967, pp. 281-297.
42. P. Masson and W. Pieczynski, "SEM algorithm and unsupervised statistical segmentation of satellite images," IEEE Trans. Geosci. Remote Sensing, vol. 31, pp. 618-633, May 1993.
43. P. McCullagh and J. A. Neider, Generalized Linear Models, 2nd ed. London, U.K.: Chapman & Hall, 1989.
44. J. Moody and C. Darken, "Fast learning in networks of locally-tuned processing units," Neural Computing., vol. I, pp. 281-294.
45. D. Nauck, U. Nauck, and R. Kruse, "Generating classification rules with the neuro-fuzzy system NEFCLASS," in Proc. Biennial Conf. North American Fuzzy Information Processing Soc., Berkeley, CA, 1996..
46. D. Nauck and F. Klawonn, "Neuro-fuzzy classification initialized by fuzzy clustering," in Proc. 4th Enrop. Congr. Intelligent Techniques and Soft Computing, Aachen, Germany, 1996.
47. S. J. Nowlan and G. E. Hinton, "Evaluation of adaptive mixtures of competing experts," in Advance in Neural Information Processing Systems, vol. 3. San Mateo, CA; Morgan Kaufmann, 1991, pp. 774-780.
48. K. Popat and R. W. Picard, "Cluster-based probability model and its applications to image and texture processing," IEEE Trans. Image Processing, vol. 6, pp. 268-284, Feb. 1997.
49. Y. Wang, A. Reibman, F. Juang, T. Chen, and S.Y. Kung, Eds., Proc. IEEE Workshop Multimedia Signal Processing, Princeton, NJ, 1997.
50. T. Poggio and F. Girosi, "Networks for approximation and learning," Proc. IEEE, vol. 78, pp. 1481-1497, Sept. 1990.
51. J. C. Rajapakse, J. N. Giedd, and J. L. Rapoport, "Statistical approach to segmentation of single-channel cerebral MR images," IEEE Trans. Med. Imaging, vol. 16, pp. 176-185, Apr. 1997.
52. D. E. Rumelhart, J. L. McClelland, and the PDP Research Group, Parallel Distributed Processing (PDP): Exploration in the Microslriicture of Cognition, vol. I. Cambridge, MA: MIT Press, 1986.
53. J. S. Taur and S. Y. Kung, "Fuzzy decision neural networks and applications to data fusion," in Proc. IEEE Workshop Neural Nernvrks for Signal Processing, vol. Ill, Linthicum Heights, .. MD, Sept. 1993, pp. 171-180.
54. J. S. Taur and C. W. Tao, "Texture classification using a fuzzy texture spectrum and neural network," J. Electron. Imaging, vol. 7, no. I, pp. 29-35, Jan. 1998.
55. _, "Face detection using neuro-fuzzy classifiers," in Proc. Int. Symp. Multimedia Information Processing, Taiwan, Dec. 1998, pp. 309-314.
56. D. Valentin, H. Abdi, A. J. O'tool, and G. W. Cottrell, "Connectionist models of face processing: A survey," Pattern Recognition, vol. 27, pp. 1209-1230, 1994.
57. L. X. Wang, Adaptive Fuzzy Systems and Control: Design and Stability Analysis. Englewood Cliffs, NJ: Prentice-Hall, 1995.
58. Y. Wang, S. H. Lin, H. Li, and S. Y. Rung, "Data mapping by probabilistic modular networks and information theoretic criteria," IEEE Trans. Signal Processing, vol. 46, pp. 3378-3397, Dec. 1998.
59. Y. Wang, T. Adah, S. Y. Kung, and Z. Szabo, "Quantification and segmentation of brain tissue from MR images: A probabilistic neural network approach," IEEE Trans. Image Processing (Special Issue on Applications of Neural Nenvrks to Image Processing), vol. 7, pp. 1165-1181, Aug. 1998.
60. Y. Wang, T. Adah, C. Lau, and S. Y. Kung, "Quantitative analysis of MR brain image sequences by adaptive self-organizing mixtures," J. VLSI Signal Processing Syst. (Special Issue on Neural Neftvorks for Biomédical Image Processing), vol. 18, no. 3, pp. 219-2490, Apr. 1998.
61. S. R. Waterhouse, D. MacKay, and A. J. Robinson, "Bayesian methods for mixtures of experts," Proc. Advances in Neural Information Processing 8, Denver, CO, Nov. 1995, pp. 351-357.
62. M. M. Yeung, B/L. Yeo, W. Wolf, and B. Liu, "Video browsing using clustering and scene transitions on compressed sequences," in Proc. SPIE Multimedia Computing and Nertvorking, vol. 2417, Feb. 1995, pp. 399-13.
63. A. L. Yuille, P. Stolorz, and J. Utans, "Statistical physics, mixtures of distributions, and the EM algorithm," Neural Computation, vol. 6, pp. 334-340, 1994.