Introduction to the modified probabilistic neural network for general signal processing applications

IEEE Transactions on Signal Processing

Volumn 46, Issue 7, 1998, Pages 1980-1990

  Zaknich, Anthony ^a  

^a UNIVERSITY OF WESTERN AUSTRALIA   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

PROBABILITY; REGRESSION ANALYSIS; SIGNAL PROCESSING; SONAR; WAVEFORM ANALYSIS;

MODIFIED PROBABILISTIC NEURAL NETWORKS (MPNN); RADIAL BASIS FUNCTIONS;

NEURAL NETWORKS;

EID: 0032124116     PISSN: 1053587X     EISSN: None     Source Type: Journal    
DOI: 10.1109/78.700969     Document Type: Article

References (36)

1. A. Zaknich, "The modified probabilistic neural network for signal processing and pattern recognition," Ph.D. dissertation, Dept. Elect. Electron. Eng., Univ. Western Australia, Nedlands, May 1995.
2. A. Lapedes and R. Farber, "Nonlinear signal processing using neural networks: Prediction and system modeling," Tech. Rep. LA-UR87-2662, Los Alamos Nat. Lab., Los Alamos, NM, 1987.
3. S. Haykin, "Neural networks expand SP's horizons: Advanced algorithms for signal processing simultaneously account for nonlinearity, nonstationarity, and non-Gaussianity," IEEE Signal Processing Mag., vol. 13, pp. 249, Mar. 1996.
4. D. E. Rumelhart, D. E. Hinton, and R. J. Williams, "Learning representation by backpropagating errors," Nature, vol. 323, no. 9, pp. 533-536, 1986.
5. D. E. Rumelhart and J. L. McClelland, "Parallel distributed processing, explorations in the microstructure of cognition," in Foundations, vol 1. Cambridge, MA: MIT Press, 1986.
6. "Parallel distributed processing, explorations in the microstructure of cognition, A handbook of models, programs, and exercises," in Foundations, vol 1. Cambridge, MA: MIT Press, 1986.
7. P. J. Werbos, "Beyond regression: New tools for prediction and analysis in the behavioral sciences," Ph.D. dissertation, Comm. Appl. Math., Harvard Univ., Cambridge, MA, Nov. 1974.
8. M. J. D. Powell, "Radial basis functions for multivariate interpolation: A review," Tech. Rep. DAMPT 1985/NA12, Dept. Appl. Math. Theoretical Phys., Cambridge Univ., Cambridge, U.K., 1985.
9. D. S. Broomhead and D. Löwe, "Radial basis-functions, multi-variable functional interpolation and adaptive networks," Royal Signals Radar Est. Memo. 4148, Mar. 28, 1988.
10. J. S. Albus, "A new approach to manipulator control: The cerebellar model articulation controller (CMAC)," J. Dyn. Syst., Meas., Contr., Sept. 1975, pp. 220-227.
11. W. T. Miller, F. H. Glanz, and L. G. Kraft, "CMAC: An association neural network alternative to backpropagation," Proc. IEEE, vol. 78, pp. 1561-1567, Oct. 1990.
12. D. F. Specht, "A general regression neural network," IEEE Trans. Neural Networks, vol. 2, pp. 568-576, Nov. 1991.
13. A. Zaknich, C. deSilva, and Y. Attikiouzel, "The probabilistic neural network for nonlinear time series analysis," in Proc. IEEE Int. Joint Conf. Neural Networks (IJCNN), Singapore, Nov. 17-21, 1991, pp. 1530-1535; republished in V. R. Vemuri and R. D. Rogers, Artificial Neural Networks: Forecasting Times Series. New York: IEEE Comput. Soc., 1994, pp. 83-88.
14. A. Zaknich and Y. Attikiouzel, "Time series characterization schemes for the modified probabilistic neural network," Austral. J. Intelligent Inform. Process. Syst. (AJIIPS), vol. 2, no. 2, pp. 1-11, Winter 1995.
15. G. A. Carpenter and S. A. Grossberg, "ART2: Self-organization of stable category recognition codes for analog input patterns," Appl. Opt., vol. 26, no. 3, pp. 4919930, 1987.
16. "The ART of adaptive pattern recognition by a self-organizing neural network," IEEE Comput. Mag., pp. 77-88, Mar. 1988.
17. T. Kohonen, "Self-organized formation of topologically correct feature maps," Biolog. Cybern., vol. 43, pp. 59-69, 1982.
18. "The self-organizing map," Proc. IEEE, vol. 78, pp. 1464-1480, 1990.
19. "Self-organizing maps: Optimization approaches," in Proc. Int. Conf. Artif. Neural Networks, Espoo, Finland, June 1991, pp. 981-990.
20. D. R. Hush and B. G. Home, "Progress in supervised neural networks," IEEE Signal Processing Mag., vol. 10, pp. 8-39, Jan. 1993.
21. J. A. R. Biais, Estimation and Spectral Analysis. Calgary, Alta., Canada: Univ. Calgary Press, 1988.
22. D. Birkes and Y. Dodge, Alternative Methods of Regression. New York: Wiley, 1993.
23. T. Poggio and F. Girosi, "Networks for approximation and learning," Proc. IEEE, vol. 78, pp. 1481-1497, Sept. 1990.
24. F. L. Lewis, Optimal Estimation: With an Introduction to Stochastic Control Theory. New York: Wiley, 1986.
25. D. F. Specht, "Probabilistic neural networks," Int. Neural Network Soc., Neural Networks, vol. 3, p. 109-118, 1990.
26. J. Moody and C. Darken, "Fast learning in networks of locally-tuned processing units," Neural Comput., vol. 1, no. 2, pp. 281-294, Sept. 1989.
27. "Learning localizzed receptive fields," in Proc. 1988 Connectionist Models Summer Sch., Touretzky, Hinton, and Sejnowski, Eds. San rancisco, CA: Morgan-Kaufmann, 1988, pp. 133-143.
28. E. A. Nadaraya, "On estimating regression," Theory Probab. Appl, vol. 9, pp. 141-142, 1964.
29. G. S. Watson, "Smooth regression analysis," Sankhya Ser. A, vol. 26, pp. 359-372, 1964.
30. A. Zaknich and Y. Attikiouzel, "Automatic optimization of the modified probabilistic neural network for pattern recognition and time series analysis," in Proc. First Austral. New Zealand Conf. Intelligent Inform. Syst., Perth, Australia, Dec. 1-3, 1993, pp. 152-156.
31. W. H. Press, B. P. Flannery, S. A. Teukolsky, and W. T. Vetterling, Numerical Recipies. The Art of Scientific Computing. Cambridge, U.K.: Cambridge Univ. Press, 1986, pp. 283-286.
32. A. Zaknich and Y. Attikiouzel, "Application of the modified probabilistic neural network to the enhancement of noisy short wave radio time and morse code signals," Austral. J. Intelligent Inform. Process. Syst. (AJIIPS), vol. 2, no. 3, pp. 9-14, Spring 1995.
33. "The modified probabilistic neural network as a nonlinear correlator detector," in Proc. IEEE Int. Conf. Neural Networks (ICNN), Perth, Australia, Dec. 1-3, 1995, pp. 309-313.
34. "Application of artificial neural networks to nonlinear signal processing," in Computational Intelligence: A Dynamic Systems Perspective. New York: IEEE, 1995, pp. 292-308.
35. S. Haykin, "Neural Networks, A Comprehensive Foundation. New York: Macmillan, 1994.
36. A. Zaknich and Y. Attikiouzel, "Modified probabilistic neural network hardware implementation schemes," in Proc. IEEE Region 10 Conf., Digital Signal Process. Applicat., Perth, Australia, Nov. 27-29, 1996, vol. 1, pp. 167-171.