Survey of Neural Network Technology for Automatic Target Recognition

IEEE Transactions on Neural Networks

Volumn 1, Issue 1, 1990, Pages 28-43

  Roth, Michael W ^a  

^a JOHNS HOPKINS UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER PROGRAMMING--ALGORITHMS; IMAGE PROCESSING; SYSTEMS SCIENCE AND CYBERNETICS--NEURAL NETS; VISION--ARTIFICIAL;

AUTOMATIC TARGET RECOGNITION; FEATURE EXTRACTION; LEARNING ALGORITHMS; NEUROCOMPUTER HARDWARE;

PATTERN RECOGNITION;

EID: 0025404523     PISSN: 10459227     EISSN: 19410093     Source Type: Journal    
DOI: 10.1109/72.80203     Document Type: Article

References (115)

1. DARPA Neural Network Study. Fairfax, VA: AFCEA International Press, 1988.
2. B. Bhanu, “Automatic target recognition: State of the art survey,” IEEE Trans. Aerospace Electron. Syst., vol. AES-22, no. 4, pp. 364-379, July 1986.
3. M. Gyer et al., “Automatic target recognition panel report,” SAIC, San Diego, CA, rep. ET-TAR-002, Dec. 1988.
4. S. Grossberg, “Nonlinear neural networks: Principles, mechanisms, and architectures,” Neural Networks, vol. 1, pp. 16-61, 1988.
5. D. W. Ruck, S. K. Rogers, P. S. Maybeck, and M. Kabrisky, “Back propagation: A degenerate Kaiman filter?,” Wright-Patterson AFB, OH, Air Force Institute of Technology preprint, 1989.
6. A. Lapedes and R. Farber, “Nonlinear signal processing using neural networks: Prediction and system modeling,” Los Alamos Nat. Lab., Los Alamos, NM, preprint LA-UR-87-2662, July 1987.
7. D. Marr, Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. San Francisco, CA: W. H. Freeman, 1982.
8. T. Poggio, V. Torre, and C. Koch, “Computational vision and regularization theory,” Nature, vol. 317, pp. 314-319, Sept. 1985.
9. S. Geman and D. Geman, “Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images,” IEEE Trans. Patt. Anal. Mach. Intel., vol. PAMI-6, no. 6, pp. 721-741, Nov. 1984.
10. J. Marroquin, S. Mitter, and T. Poggio, “Probabilistic solution of ill-posed problems in computational vision,” J. Amer. Stat. Ass., vol. 82, no. 397, pp. 76-89, Mar. 1987.
11. J. Besag, “Spatial interaction and the statistical analysis of lattice systems ‘with discussion’,” J. Royal Stat. Soc, ser. B, vol. 36, pp. 192-326, 1974.
12. B. Roysam and M. I. Miller, “Mapping deterministic rules to stochastic representations via Gibbs’ distributions on massively parallel analog networks: Application to global optimization,” in Proc. 1988 Connectionist Models Summer School, D. Touretzky, G. Hinten, and T. Sejnowski, Eds. San Mateo, CA: Morgan Kaufmann, 1989, pp. 229-238.
13. M. I. Miller, B. Roysam, and K. R. Smith, “Mapping rule-based and stochastic constraints to connection architectures: Implication for hierarchical image processing,” Proc. SPIE Int. Soc. Opt. Eng., 1988, pp. 1078-1085.
14. T. Poggio et al., “The M.I.T. vision machine,” in Proc. Image Understanding Workshop 1988. San Mateo, CA: Morgan Kaufmann, Apr. 1988, pp. 177-198.
15. G. L. Bilbro, H. Hiriyannaiah, and W. E. Snyder, “Fusion of range and reflectance image data using Markov random fields,” in Proc. 1988 IEEE Int. Symp. Intelligent Control. Piscataway, NJ: IEEE, 1988.
16. S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, “Optimization by simulated annealing,” Science, vol. 220, pp. 671-680, 1983.
17. D. Geman and S. Geman, “Bayesian image analysis,” in Disordered Systems and Biological Organization, E. Bienenstock, F. Fogelman, and G. Weisbuch, Eds. Heidelberg, W. Germany: Springer-Verlag, 1986, pp. 301-319.
18. J. J. Hopfield, “Neural networks and physical systems with emergent collective computational abilities,” Proc. Nat. Acad. Sci., vol. 79, pp. 2554-2558, 1982.
19. J. J. Hopfield, “Neurons with graded response have collective computational properties like those of two-state neurons,” Proc. Nat. Acad. Sci., vol. 81, pp. 3088-3092, 1984.
20. H. P. Graf et al., “VLSI implementation of a neural network memory with several hundreds of neurons,” in Neural Networks for Computing, Snowbird, Utah, 1986, AIP Conf. Proc. 151, J. S. Denker, Ed. New York: American Institute of Physics, 1986, pp. 182-187.
21. J. J. Hopfield and D. W. Tank, “ ‘Neural’ computation of decisions in optimization problems,” Biol. Cybern., vol. 52, pp. 141-152, 1985.
22. C. Koch, J. Marroquin, and A. Yuille, “Analog neuronal networks in eary vision,” Proc. Nat. Acad. Sci., vol. 83, pp. 4263-4267, June 1986.
23. C. Koch, “Computing motion in the presence of discontinuities-Algorithm and analog networks,” in Neural Computers, R. Eckmiller and C. von der Malsburg, Eds. New York: Springer-Verlag, 1988, pp. 101-110.
24. Y. Zhou, “Artificial neural network algorithms for some computer vision problem,” Ph.D. dissertation, Univ. of Southern California, Los Angeles, CA, 1988.
25. H. Bülthoff, J. Little, and T. Poggio, “A parallel algorithm for realtime computation of optical flow,” Nature, vol. 337, pp. 549-553, Feb. 9, 1989.
26. Y. T. Zhou, R. Chellappa, A. Vaid, and B. K. Jenkins, “Image restoration using a neural network,” IEEE Trans. Acoust., Speech, Signal Processing, vol. 36, no. 7, pp. 1141-1151, July 7, 1988.
27. C. Cortes and J. A. Hertz, “A network system for image segmentation,” in IJCNN Int. Joint Conf. Neural Networks, vol. I. Piscataway, NJ: IEEE, 1989, pp. 121-125.
28. M. J. Daily, “Color image segmentation using Markov random fields,” in Proc. Image Understanding Workshop 1989. San Mateo, CA: Morgan Kaufmann, 1989, pp. 552-562.
29. D. Marr and T. Poggio, “Cooperative computations of stereo disparity,” Science, vol. 194, pp. 283-287, 1976.
30. E. Goles and G. Y. Vichniac, “Lyapunov functions for parallel neural networks,” in Neural Networks for Computing, AIP Conf. Proc. 151, J. S. Denker, Ed. New York: American Institute of Physics, 1986, pp. 165-181.
31. A. F. Gmitro and G. R. Gindi, “Optical neurocomputer for implementation of the Marr-Poggio stereo algorithm,” in IEEE First Int. Conf. Neural Networks, vol. III, M. Caudill and C. Butler, Ed. Picataway, NJ: IEEE, 1987, pp. 599-606.
32. G. Z. Sun, H. H. Chen, and Y. C. Lee, “Learning stereopsis with neural networks,” in IEEE First Int. Conf. Neural Networks, vol. IV, M. Caudill and C. Butler, Ed. Piscataway, NJ: IEEE, 1987, pp. 345-355.
33. Y. T. Zhou and R. Chellappa, “Stereo matching using a neural network,” in 1988 Int. Conf. Acoust., Speech, Signal Processing. Piscataway, NJ: IEEE, 1988, pp. 940-943.
34. S. P. Clifford and N. M. Nasrabadi, “Integration of stereo vision and optical flow using Markov random fields,” in IEEE Int. Conf. Neural Networks, vol. I. Piscataway, NJ: IEEE, 1988, pp. 577-584.
35. Y. T. Zhou and R. Chellappa, “Neural network algorithms for motion stereo,” in IJCNN Int. Joint Conf. Neural Networks, vol. II. Piscataway, NJ: IEEE, 1989, pp. 251-258.
36. G. Bilbro, R. Mann, T. K. Miller, W. E. Snyder, D. E. Van den Bout, and M. White, “Optimization by mean field annealing,” in Advances in Neural Information Processing Systems 1, D. S. Touretzky, Ed. San Mateo, CA: Morgan Kaufmann, 1989, pp. 91-98.
37. G. C. Fox and W. Furmanski, “Load balancing by a neural network,” California Institute of Technology, Pasadena, CA, tech. rep. C3P363, Sept. 1986.
38. J. R. Anderson, “A mean field computational model for PDP,” in Proc. 1988 Connectionist Models Summer School, D. Touretzky. G. Hinton, and T. Sejnowski, Eds. San Mateo, CA: Morgan Kaufmann, 1989, pp. 217-223.
39. G. L. Bilbro and W. E. Snyder, “Range image restoration using mean field annealing,” in Advances in Neural Information Processing Systems I, D. S. Touretzky, Ed. San Mateo, CA: Morgan Kaufmann, 1989, pp. 594-601.
40. H. Hiriyannaiah, G. L. Bilbro, W. E. Snyder, and R. Mann, “Restoration of piecewise constant images via mean field annealing,” J. Opt. Soc. Amer., to be published.
41. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning internal representations by error propagation,” in Parallel Distributed Processing, Explorations in the Microstructure of Cognition. D. E. Rumelhart and J. L. McClelland, Eds. Cambridge, MA: M.I.T. Press, 1986, pp. 318-362.
42. F. Rosenblatt, Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms. Washington, DC: Spartan, 1961.
43. M. Minsky, and S. Papert, Perceptrons. Cambridge, MA: M.I.T. Press, 1969.
44. M. W. Roth, “Neural-network technology and its applications,” Heuristics, vol. 2, no. 1, pp. 46-62, 1989 ‘reprinted version’; also originally appeared in The Johns Hopkins APL Tech. Dig., vol. 9, no. 3, pp. 242-253, 1988.
45. B. Widrow and M. E. Hoff “Adaptive switching circuits,” in IRE Western Electron. Show and Conv., pt. 4, 1960, pp. 96-104.
46. G. E. Hinton, “Learning distributed representations of concepts,” in Eighth Annu. Conf. Cognitive Sci. Soc. Hillsdale, NJ: Lawrence Erlbaum, 1986, pp. 1-12.
47. T. Kohonen, Self-Organization and Associative Memory. Berlin: Springer-Verlag, 1984.
48. E. Saund, “Abstraction and representation of continuous variables in connectionist networks,” in Proc. Fifth Nat. Conf. Artificial Intell. San Mateo, CA, Morgan Kaufmann, 1986, pp. 638-644.
49. E. B. Baum and F. Wilczek, “Supervised learning of probability distributions by neural networks,” in Neural Information Processing Systems, D. Z. Anderson, Ed. American Institute of Physics, 1988, pp. 52-61.
50. S. E. Fahlman, “Faster learning variations on back propagation: An empirical study,” in Proc. 1988 Connectionist Models Summer School, D. Touretzky, G. Hinton, and T. Sejnowski, Eds. San Mateo, CA: Morgan Kaufmann, 1989, pp. 38-51.
51. E. B. Baum, “What size net gives valid generalization?,” Neural Computation, vol. 1, no. 1, pp. 151-160, 1989.
52. F. J. Pineda, “Generalization of back propagation to recurrent neural networks,” Phys. Rev. Lett., vol. 59, pp. 2229-2232, 1987; also “Generalization of back propagation to recurrent and high-order networks,” in Neural Information Processing Systems, D. Z. Anderson, Ed. New York: American Institute of Physics, 1988, pp. 602-611.
53. F. J. Pineda, “Dynamics and architecture in neural computation,” J. Complexity, vol. 4, pp. 216-245, 1988.
54. N. Qian and T. J. Sejnowski, “Learning to solve random-dot stereograms of dense and transparent surfaces with recurrent back propagation,” in Proc. 1988 Connectionist Models Summer School, D. Touretzky, G. Hinton, and T. Sejnowski, Eds. San Mateo, CA: Morgan Kaufmann, 1989, pp. 435-443.
55. J. M. Oyster, F. Vicuna, and W. Broadwell, “Associative network applications to low-level machine vision,” Appl. Opt., vol. 26, no. 10, pp. 1919-1926, 1987.
56. M. W. Roth, “Neural networks for extraction of weak targets in high clutter environments,” IEEE Trans. Syst., Man., Cybern., vol. 19, no. 5, pp. 1210-1217, 1989.
57. J. G. Daugman, “Relaxation neural network for nonorthogonal image transforms,” in IEEE Int. Conf. Neural Networks, vol. I. Piscataway, NJ: IEEE, 1988, pp. 547-560.
58. J. Jones and L. Palmer, “An evaluation of the twodimensional Gabor filter model of simple receptive fields in cat striate cortex,” J. Neurophysiol., vol. 58, pp. 1233-1258, 1987.
59. P. K. Kienker, T. J. Sejnowski, G. E. Hinton, and L. E. Schumacher, “Separating figure from ground with a parallel network,” Perception, vol. 15, pp. 197-216, 1986.
60. N. M. Nasrabadi and Y. Feng, “Vector quantization of images based upon the Kohonen self-organizing feature maps,” in IEEE Int. Conf. Neural Networks, 1988, pp. I-101-I-108.
61. Y. Linde, A. Buzo, amd R. M. Gray, “An algorithm for vector quantizer design,” IEEE Trans. Commun., vol. COM-28, pp. 84-95, Jan. 1980.
62. G. W. Cottrell, P. Munro, and D. Zipser, “Image compression by back propagation: An example of extensional programming,” in Models of Cognition: A Review of Cognitive Science, vol. 1, N. E. Sharkey, Ed. Norwood, NJ: Ablex, pp. 208-240, 1989.
63. R. M. Kuczewski, M. H. Myers, and W. J. Crawford, “Exploration of backward error propagation as a self-organizational structure,” in IEEE First Int. Conf. Neural Networks, vol. II, M. Caudill and C. Butler, Eds. Piscataway, NJ: IEEE, 1987, pp. 89-95.
64. P. Baldi, and K. Hornik, “Neural networks and principal component analysis: Learning from examples without local minima,” Neural Networks, vol. 2, pp. 53-58, 1989.
65. G. W. Cottrell and P. Munro, “Principal components analysis of images via back propagation,” in Proc. SPIE Int. Soc. Opt. Eng., pp. 1070-1077, 1988.
66. R. L. Kulp and A. S. Gilbert, “Combination of dissimilar features in neural networks for ship recognition,” Johns Hopkins Univ. Appl. Phys. Lab. rep. FIA‘1’-89U-022, Oct. 31, 1989.
67. L. B. Moorehead and R. A. Jones, “A neural network for shape recognition,” in IEEE Region 5 Conf. 1988. Piscataway, NJ: IEEE, 1988, pp. 187-191.
68. D. T. Lawton, T. S. Levitt, and P. Gelband, “Knowledge based vision for terrestrial robots,” in Proc. Image Understanding Workshop, 1988. San Mateo, CA: Morgan Kaufmann, 1988, pp. 103-110.
69. R. Mohan and R. Nevatia, “Preceptual grouping for the detection and description of structures in aerial images,” in Proc. Image Understanding Workshop 1988. San Mateo, CA: Morgan Kaufmann, 1988, pp. 512-526.
70. T. A. Jamison and R. J. Schalkoff, “Image labelling: A neural network approach,” Image and Vision Computing, vol. 6, no. 4, pp. 203-213, Nov. 1988.
71. A. Pentland, “Part segmentation for object recognition,” Neural Computation, vol. 1, no. 1, pp. 82-91, 1989.
72. W. Y. Huang and R. P. Lippmann, “Comparisons between neural net and conventional classifiers,” in IEEE First Int. Conf. Neural Networks, vol. IV, M. Caudill and C. Butler, Eds. Piscataway. NJ: IEEE, 1987, pp. 485-492.
73. D. E. Glover, “An optical Fourier/electronic neurocomputer automated inspection system,” in IEEE Int. Conf. Neural Networks, vol. I. Piscataway, NJ: IEEE, 1988, pp. 569-576.
74. S. E. Troxel, S. K. Rogers, and M. Kabrisky, “The use of neural networks in PSRI target recognition,” in IEEE Int. Conf. Neural Networks, vol. I. 1988, pp. 593-600.
75. S. D. Post, “A bipartite connectionist model to represent N-ary Boolean and linear constraints,” in IEEE First Int. Conf. Neural Networks, M. Caudill, and C. Butler, Eds. Piscataway, NJ: IEEE, 1987, pp. III-87-III-94.
76. S. I. Gallant, “Connectionist expert systems,” Commun. Ass. Comput. Mach., vol. 31, no. 2, pp. 152-169, 1988.
77. D. G. Bounds, P. J. Lloyd, B. Mathew, and G. Waddell, “A multilayer perceptron network for the diagnosis of low back pain,” in IEEE Int. Conf. Neural Networks, vol. II, 1988, pp. 481-489.
78. G. Bradshaw, R. Fozzard, and L. Ceci, “A connectionist expert system that actually works,” in Advances in Neural Information Processing Systems I, D. S. Touretzky, Ed. San Mateo, CA: Morgan Kaufmann, 1989, pp. 248-255.
79. J. S. Denker et al., “Neural network recognizer for handwritten zip code digits,” in Advances in Neural Information Processing Systems I, D. S. Touretzky, Ed. San Mateo, CA: Morgan Kaufmann, 1989, pp. 323-331.
80. R. Hecht-Nielsen, “Neurocomputing: Picking the human brain,” IEEE Spectrum, vol. 25, no. 3, pp. 36-41, 1988.
81. C. Mead, Analog VLSI and Neural Systems. New York: Addison-Wesley, 1989.
82. H. P. Graf and L. D. Jackel, “Analog electronic neural network circuits,” IEEE Circuits, Devices Mag., pp. 44-49, 55, July 1989.
83. C. A. Mead and M. A. Mahowald, “A silicon model of early visual processing,” Neural Networks, vol. 1, pp. 91-97, 1988.
84. C. Koch, J. Luo, C. Mead, and J. Hutchinson, “Computing motion using resistive networks,” Proc. SPIE Int. Soc. Opt. Eng., pp. 108-113, 1988; also J. Hutchinson, C. Koch, J. Luo, and C. Mead, “Computing motion using analog and binary resistive networks,” Computer, pp. 52-63, Mar. 1988.
85. S. P. DeWeerth and C. A. Mead, “A Two-dimensional visual tracking array,” in Advance Research in VLSI, J. Allen and F. T. Leighton, Eds. Cambridge, MA: M.I.T. Press, 1988, pp. 259-275.
86. J. Harris, C. Koch, J. Luo, and J. Wyatt, “Resistive fuses: Analog hardware for detecting discontinuities in early vision,” in Analog VLSI Implementation of Neural Systems, C. Mead and M. Ismail, Eds. Boston, MA: Kluwer Academic, 1989, pp. 27-55.
87. M. A. Mahowald and T. Delbrück, “Cooperative stereo matching using static and dynamic image features,” in Analog VLSI Implementation of Neural Systems, C. Mead and M. Ismail, Eds. Boston, MA: Kluwer Academic, 1989, pp. 213-238.
88. R. Allen, C. Mead, F. Faggin, and G. Gribble, “Orientation-selective VLSI retina,” Proc. SPIE Int. Soc. Opt. Eng., pp. 1040-1046, 1988.
89. C. Mead, “Adaptive retina,” in Analog VLSI Implementation of Neural Systems, C. Mead and M. Ismail, Eds. Boston, MA: Kluwer Academic, 1989, pp. 239-246.
90. D. L. Standley and J. L. Wyatt, “Stability criterion for lateral inhibition and related networks that is robust in the presence of integrated circuit parasitics,” IEEE Trans. Circuits, Syst., vol. 36, no. 5, pp. 675-681, 1989.
91. M. J. S. Smith and C. L. Portmann, “Practical design and analysis of a simple ‘neural’ optimization circuit,” IEEE Trans. Circuits Syst., vol. 36, no. 1, pp. 42-50, Jan. 1989.
92. J. I. Raffel, “The application of wafer-scale technology to neuromorphic systems,” in Advanced Research in VLSI, J. Allen and F. T. Leighton, Eds. Cambridge, MA: M.I.T. Press, 1988, pp. 121-129.
93. J. I. Raffel, J. R. Mann, R. Berger, A. M. Soares, and S. Gilbert, “A generic architecture for wafer-scale neuromorphic systems,” Lincoln Lab. J., vol. 2, no. 1, pp. 63-75, 1989.
94. J. Alspector, R. B. Allen, V. Hu, and S. Satyanarayana, “Stochastic learning networks and their electronic implementation,” in Neural Information Processing Systems, Denver, Co., 1987, D. A. Anderson, Ed. American Institute of Physics, 1988, pp. 9–21.
95. D. B. Schwartz, R. E. Howard, and W. E. Hubbard, “Adaptive neural networks using MOS charge storage,” in Advances in Neural Information Processing Systems I, D. S. Touretzky, Ed. San Mateo, CA: Morgan Kaufmann, 1989, pp. 761-768.
96. M. Holler, S. Tam, H. Castro, and R. Benson, “An electrically trainable artificial neural network ‘ETANN’ with 10240 ‘floating gate’ synapses,” in IJCNN Int. Joint Conf. Neural Networks, vol. II. Piscataway, NJ: IEEE, 1989, pp. 191-196.
97. Y. P. Tsividis, “Analog MOS integrated circuits—certain new ideas, trends, and obstacles,” IEEE J. Solid-State Circuits, vol. SC-22, no. 3, pp. 317-321, June 1987.
98. S. Bibyk and M. Ismail, “Issues in analog VLSI and MOS techniques for neural computing,” in Analog VLSI Implementation of Neural Systems, C. Mead and M. Ismail, Eds. Boston, MA: Kluwer Academic, 1989, pp. 103-133.
99. Y. P. Tsividis and D. Anastassiou, “Switched-capacitor neural networks,” Electron. Lett., vol. 23, no. 18, pp. 958-959, Aug. 27, 1987.
100. Y. P. Tsividis and S. Satyanarayana, “Analogue circuits for variable-synapse electronic neural networks,” Electron. Lett., vol. 23, no. 24, pp. 1313-1314, Nov. 19, 1987.
101. Y. S. Abu-Mostafa and D. Psaltis, “Optical neural computers.” Sci. Amer., vol. 256, no. 3, pp. 88-95, 1987.
102. T. Williams, “Optics and neural nets—trying to model the human brain,” Computer Design, pp. 47-62, Mar. 1, 1987.
103. D. Psaltis, and N. Farhat, “Optical information processing based on an associative-memory model of neural nets with thresholding and feedback,” Opt. Lett., vol. 10, pp. 98-100, 1985.
104. N. H. Farhat, D. Psaltis, A. Prata, and E. Paek, “Optical implementation of the Hopfield model,” Appl. Opt., vol. 24, pp. 1469-1475, 1985.
105. G. D. Boyd, “Optically excited synapse for neural networks,” Appl. Opt., vol. 26, pp. 2712-2719, 1987.
106. C. D. Kornfeld, R. C. Frye, C. C. Wong, and E. A. Rietman, “An optically programmed neural network,” in IEEE Int. Conf. Neural Networks, vol. II, 1988, pp. 357-364.
107. N. H. Farhat, “Optoelectronic analogs of self-programming neural nets—architecture and methodologies for implementing fast stochastic learning by simulated annealing,” Appl. Opt., vol. 26, pp. 5093-5103, 1987.
108. R. A. Athale and C. W. Stirk, “Compact architectures for adaptive neural nets,” Opt. Eng., vol. 28, no. 4, pp. 447-455, Apr. 1989.
109. D. Psaltis, K. Wagner, and D. Brady, “Learning in optical neural computers,” in IEEE First Int. Conf. on Neural Networks, M. Caudill and C. Butler, Eds. Piscataway, NJ: IEEE, vol. III, 1987, pp. 549-555; also D. Psaltis, D. Brady, and K. Wagner, “Adaptive optical networks using photorefractive crystals,” Appl. Opt., vol. 27, no. 9, pp. 1752-1759, 1988.
110. H. J. White, N. B. Aldridge, and I. Lindsay, “Digital and analog holographic associative memories,” Opt. Eng., vol. 27, no. 1, pp. 30-37, 1988.
111. K. Wagner, and D. Psaltis, “Multilayer optical learning networks,” Appl. Opt., vol. 26, pp. 5061-5076, 1987.
112. Y. Owechko, “Optoelectronic resonator neural networks,” Appl. Opt., vol. 26, pp. 5104-5111, 1987.
113. H. J. Caulfield, “Parallel N4 optical interconnections,” in IEEE First Int. Conf. Neural Networks, vol. III, M. Caudill and C. Butler, Eds. Piscataway, NJ: IEEE, 1987, pp. 595-597.
114. K. Hsu and D. Psaltis, “Invariance and discrimination properties of the optical associative loop,” in IEEE Int. Conf. Neural Networks, 1988, pp. 395-402.
115. H. M. Stoll and L. S. Lee, “A continuous-time optical neural network,” in IEEE Int. Conf. Neural Networks, vol. II, 1988, pp. 373-384.