A Neural Network Based Hybrid System for Detection, Characterization, and Classification of Short-Duration Oceanic Signals

IEEE Journal of Oceanic Engineering

Volumn 17, Issue 4, 1992, Pages 351-363

  Ghosh, Joydeep ^a   Deuser, Larry M ^b   Beck, Steven D ^b  

^a UNIVERSITY OF TEXAS AT AUSTIN   (United States)

^b Tracor Applied Sciences ^*  (United States)

Author keywords

evidence combination; feature extraction; Neural networks; passive sonar; pattern classification; short duration oceanic signals

Indexed keywords

INFORMATION THEORY; NEURAL NETWORKS; OCEANOGRAPHY; PATTERN RECOGNITION; RANDOM PROCESSES; SIGNAL PROCESSING; SPECTRUM ANALYSIS;

NEURAL NETWORKS CLASSIFIERS; PASSIVE SONAR; SHORT-DURATION OCEANIC SIGNALS; WAVELET-BASED FEATURE EXTRACTION;

SONAR;

EID: 0026939766     PISSN: 03649059     EISSN: 15581691     Source Type: Journal    
DOI: 10.1109/48.180304     Document Type: Article

References (65)

1. L. Deuser and D. Middleton, “On the classification of underwater acoustic signals: An envinomentally adaptive approach,” The Acoustic Society of America, vol. 65, pp. 438–443, 1979.
2. C. H. Chen, “Automatic recognition of underwater transient signals-a review,” in Proc. ICASSP, pp. 1270–1272, 1985.
3. Special Issue, “Underwater Acoustic Signal Processing,” IEEE J. Ocean. Eng., p. 2–278, Jan. 1987.
4. R. J. Urick, Principles of Underwater Sound. New York: McGraw-Hill, 2nd Ed., 1975.
5. W. Y. Huang and R. P. Lippmann, “Neural network and traditional classifiers,” Neural Inform. Processing Syst., pp. 387–396, 1987.
6. R. P. Lippmann, “Review of neural networks for speech recognition,” Neural Computation, vol. 1, pp. 1–38, 1989.
7. J. Ghosh and K. Hwang, “Mapping neural networks onto message-passing multicomputers,” J. Parallel and Distributed Computing, vol. 6, pp. 291–330, Apr. 1989.
8. K. Ng and R. P. Lippmann, “Practical characteristics of neural network and conventional pattern classifiers,” in Advances in Neural Information Processing Systems—III, pp. 970–976, 1991.
9. Y. L. Cun, J. S. Denker, and S. A. Solla, “Optimal brain damage,” in Advances in Neural Information Processing Systems—II, pp. 598–605, 1990.
10. S. Beck and J. Ghosh, “Noise sensitivity of static neural classifiers,” in SPIE Conf. Sci Artificial Neural Networks SPIE Proc., vol. 1709, Apr. 1992.
11. D. Lowe and A. R. Webb, “Optimized feature extraction and the bayes decision in feed-forward classifier networks,” IEEE Trans. PAMI, vol. 13, pp. 355–364, Apr. 1991.
12. J. Makhoul, “Pattern recognition properties of neural networks,” in Proc. 1st IEEE Workshop on Neural Networks for Signal Processing, pp. 173–187, Sept. 1991.
13. J. Denker et al. “Large automatic learning, rule extraction and generalization,” Complex Systems, vol. 1, pp. 877–922, 1987.
14. E. Levin, N. Tishby, and S. A. Solla, “A statistical approach to learning and generalization in layered neural networks,” Proc. IEEE, vol. 78, pp. 1568–74, Oct. 1990.
15. J. E. Moody, “Note on generalization, regularization and architecture selection in nonlinear learning systems,” in IEEE Workshop Neural Networks for Signal Processing, pp. 1–10, 1991.
16. P. J. Werbos, “Links between artificial neural networks and statistical pattern recognition,” in I. K. Sethi and A. Jain, Eds., Artificial Neural Networks and Statistical Pattern Recognition. Amsterdam: Elsevier Science, 1991, pp. 11–32.
17. S. Raudys and A. K. Jain, “Processing of textured images using neural networks,” in I. K. Sethi and A. Jain, Ed. Artificial Neural Networks and Statistical Pattern Recognition. Amsterdam: Elsevier Science, 1991, pp. 33–50.
18. J. Ghosh et al., “Adaptive kernel classifiers for short-duration oceanic signals,” in IEEE Conf. Neural Networks for Ocean Engineering, pp. 41–48, Aug. 1991.
19. J. Ghosh, L. Deuser, and S. Beck, “Impact of feature vector selection on static classification of acoustic transient signals,” in Government Neural Network Applications Workshop, Aug. 1990.
20. Y. Shin and J. Ghosh, “The pi-sigma network: An efficient higherorder network for pattern classification and function approximation,” in Proc. Joint Conf. Neural Networks, pp. I: 13–18, July 1991.
21. S. Beck, L. Deuser, R. Still, and J. Whiteley, “A hybrid neural network classifier of short duration acoustic signals,” in Proc. IJCNN, pp. 1:119-124, July 1991.
22. B. Widrow and S. D. Stearns, Adaptive Signal Processing. Englewood Cliffs, NJ: Prentice-Hall, 1985.
23. R. P. Gorman and T. J. Sejnowski, “Analysis of hidden units in a layered network trained to classify sonar targets,” Neural Networks, pp. 1:75-89, 1988.
24. J. Specht, “Probabilistic neural networks,” Neural Neworks, pp. 45–74, 1990.
25. J. M. Combes, A. Grossman, and P. Tchamitchian, Eds., Wavelets: Time-Frequency Methods and Phase Space. New York: Springer-Verlag, 1989.
26. Y. H. Pao, T. L. Hemminger, D. J. Adams, and S. Clary, “An episodal neural-net computing approach to the detection and interpretation of underwater acoustic transients,” in Conf. Neural Networks for Ocean Eng., pp. 21–28, 1991.
27. O. Rioul and M. Vetterli, “Wavelets and signal processing,” IEEE Signal Processing Magazine, pp. 14–38, Oct. 1991.
28. T. Lefebvre, J. M. Nicolas, and P. Degoul, “Numerical to symbolical conversion for acoustic signal classification using a two-stage neural architecture,” in Proc. Int. Neural Network Conf., pp. 119–122, June 1990.
29. S. Geman, E. Bienenstock, and R. Doursat, “Neural networks and the bias/variance dilemma,” Neural Computation, vol. 4, pp. 1–58, 1992.
30. T. D. Sanger, “Optimal unsupervised learning in a singe-layer linear feedforward neural network,” Neural Networks, vol. 2, pp. 459–474, 1989.
31. T. Kohonen, Self-Organization and Associative Memory. Berlin: Springer-Verlag, 3rd ed., 1989.
32. R. P. Lippmann, “Pattern classification using neural networks,” IEEE Commun. Mag., pp. 47–64, Nov. 1989.
33. K. Ng and R. P. Lippmann, “A comparative study of the practical characteristics of neural network and conventional pattern classifiers,” Advances in Neural Information Processing Systems—III, pp. 970–975, 1990.
34. D. S. Broomhead and D. Lowe, “Multivariable functional interpolation and adaptive networks,” Complex Systems, vol. 2, pp. 321–355, 1988.
35. J. Moody and C. J. Darken, “Fast learning in networks of locally-tuned processing units,” Neural Computation, vol. 1, pp. 281–294, 1989.
36. P. K. Simpson, “Fuzzy min-max classification with neural networks,” in Proc. IEEE Conf Neural Networks for Ocean Eng., pp. 291–300, Aug. 1991.
37. T. Kohonen, G. Barna, and R. Chrisley, “Statistical pattern recognition with neural networks: Benchmarking studies,” in IEEE Annual Int. Conf. Neural Networks, July 1988.
38. S. M. Weiss and C. A. Kulikowski, Computer Systems That Learn, San Mateo, CA: Morgan Kaufmann, 1991.
39. M. I. Jordan, “Serial order: A parallel, distributed processing approach,” in J. L. Elman and D. E. Rumelhart, Eds., Advances in Connectionist Theory: Speech. Hillsdale: Lawrence Erlbaum, 1989.
40. J. L. Elman, “Finding structure in time,” Cognitive Science, vol. 14, pp. 179–211, 1990.
41. R. Hecht-Nielsen, Neurocomputing. Reading, MA: Addison-Wesley, 1990.
42. M. Sato, “A real time learning algorithm for recurrent analog neural networks,” Bio. Cybern., vol. 62, pp. 237–241, 1990.
43. A. G. Ivakhnenko, “Polynomial theory of complex systems,” IEEE Trans. Syst. Man, Cybern., vol. 1, pp. 364–378, Oct. 1971.
44. Y.-H. Pao, Adaptive Pattern Recognition and Neural Networks. Reading, MA: Addison-Wesley, 1989.
45. J. Kowalski, E. Hartman, and J. Keeler, “Layered neural networks with Gaussian hidden units as universal approximators,” Neural Computation, vol. 2, pp. 210–215, 1990.
46. M. Niranjan and F. Fallside, “Neural networks and radial basis functions in classifying static speech patterns,” Tech. Rep. CUED/FINFENG/TR22, 1988.
47. D. De Sieno, “Adding conscience to competitive learning,” in IEEE Annual Int. Conf. Neural Networks, pp. 1117–1124, 1988.
48. C. L. Giles and T. Maxwell, “Learning, invariance, and generalizaiton in a high-order neural network. Applied Optics, vol. 26, pp. 4972–4978, 1987.
49. M. R. Lynch and P. J. Rayner, “The properties and implementation of the non-linear vector space connectionist model,” in Proc. First IEE Int. Conf. Artificial Neural Networks, pp. 184–190, Oct. 1989.
50. J. Ghosh and Y. Shin, “Efficient higher-order networks for function approximation and classification,” IEEE Trans. Neural Networks, 1992.
51. K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural Networks, vol. 2, pp. 359–366, 1989.
52. J. Ghosh, S. Beck, and C. C. Chu, “Evidence combination techniques for robust classification of short-duration oceanic signals,” in SPIE Conf. Adaptive Learning Systems, SPIE Proc., vol. 1706, Apr. 1992.
53. S. Geva and J. Sitte. “Adaptive nearest neighbor classification,” IEEE Trans. Neural Networks, vol. 2, pp. 318–322, 1991.
54. S. Lee and R. M. Kil, “Multilayer feedforward potential function network,” In Proc. Second Int. Conf. Neural Networks, pp. 161–171, 1988.
55. T. Poggio and F. Girosi, “Networks for approximation and learning,” Proc. IEEE, vol. 78, pp. 1481–1497, Sept. 1990.
56. J. Park and I. W. Sandberg, “Universal approximation using radial basis function networks,” Neural Computation. vol. 3, pp. 246–257, 1991.
57. R. Hecht-Neilsen, “Counterpropagation networks,” Appl. Optics, vol. 26, pp. 4979–4984, 1987.
58. S. Chakravarthy, J. Ghosh, L. Deuser, and S. Beck, “Efficient training procedures for adaptive kernel classifiers,” in Neural Networks for Signal Processing, pp. 21–29, IEEE Press, 1991.
59. R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis. New York: Wiley, 1973.
60. K. Fukunaga, Introduction to Statistical Pattern Recognition, 2nd Ed. New York: Academic, 1990.
61. M. D. Richard and R. P. Lippmann, “Neural network classifiers estimate bayesian a posteriori probabilities,” Neural Computation, vol. 3, pp. 461–483, 1991.
62. H. Gish, “A probablistic approach to the understanding and training of neural network classifiers,” in Proc. Int. Conf. ASSP, pp. 1361–1364, Apr. 1990.
63. P. A. Shoemaker, M. J. Carlin, R. L. Shimabukuro, and C. E. Priebe. “Least squares learning and approximation of posterior probabilities on classification problems by neural network models,” in Proc. 2nd Workshop Neural Networks, WNN-AIND91, pp. 187–196, Feb. 1991.
64. E. H. Shortcliffe and B. G. Buchanan, “A model of inexact reasoning in medicine,” Mathematical Biosciences, vol. 23, pp. 351–379, 1975.
65. D. Heckerman, “Probabilistic interpretation for MYCIN's uncertainty factors,” in L.N. Kanal and J. F. Lemmer, Eds., Uncertainty in Artificial Intelligence. North-Holland, 1986, pp. 167–196.