A hardware annealing method for optimal solutions on cellular neural networks

IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing

Volumn 43, Issue 6, 1996, Pages 409-421

  Bang, Sa H ^a,b   Sheu, Bing J ^a,b   Chou, Eric Y ^a,b  

^a IEEE   (United States)

^b University of Southern California ^*  (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ELECTRIC NETWORK ANALYSIS; ITERATIVE METHODS; MATHEMATICAL MODELS; SIGNAL PROCESSING; SIMULATED ANNEALING;

EFFECTIVE MEAN FIELD ANNEALING; GLOBAL OPTIMIZATION; HARDWARE ANNEALING; VOLTAGE GAIN;

CELLULAR NEURAL NETWORKS;

EID: 0030156951     PISSN: 10577130     EISSN: None     Source Type: Journal    
DOI: 10.1109/82.502314     Document Type: Article

References (21)

1. P. M. Pardalos and J. B. Rosen, Constrained Global Optimization: Algorithms and Applications. Berlin; Germany, Springer-Verlag, 1987.
2. C. H. Papadimitriou and K. Steiglitz, Combinatorial Optimization: Algorithms and Complexity. Englewood Cliffs, NJ: Prentice Hall, 1982.
3. J. J. Hopfleld and D. W. Tank, 'Neural' computation of decisions in optimization problems, Biol Cybern., vol. 52, pp. 141-152, 1985.
4. D. W. Tank und J. J. Hopfield, Simple 'neural' opt i miJ.at ion networks: An A/D converter, signal decision circuit, and a linear programming circuit, IEEE Trans. Circuits Syst.. vol. 33, pp. 533-541, May 1986.
5. D. Kunz. Suboptimum solutions obtained by the Hopfield-tank neural network algorithm, Biol. Cybern., vol. 65, pp. 129-133, 1991.
6. S. Kirkpatrick, C. D. Gelatt, Jr., and M. P. Vecchi, Optimization by simulated annealing, Science, vol. 220, pp. 671-680, May 1983.
7. E. H. L. Aarts and J. II. M. Korst, Simulated Annealing and Boltzmann Machine. Chichster: Wiley, 1988.
8. J. H. M. Korst and E. H. L. Aarts, Combinatorial optimization on a Boltzmann machine, J. Parallel and Distributed Comput., vol. 6, pp. 331-357, 1989.
9. C. Peterson and J. R. Anderson, A mean field theory learning algorithm for neural networks. Complex Syst., vol. 1, no. 5, pp. 995-1019, 1987.
10. G. Bilbro et al., Optimization by mean field annealing, in Advances in Neural Information Processing Systems, D. S. Touretzky, Ed., vol. I New York: Morgan Kaufmann, 1990.
11. [11) A. Cichocki and R. Unbehauen, Neural Networks for Optimization and Signal Processing. West Sussex; England: Wiley, 1993.
12. N. Metropolis et al., Equations of state calculations by fast computing machines, J. Chem. Phys.. vol. 21, pp. 1087-1091. 1953.
13. L. 0. Chuaand L. Yang, Cellular neural network: Theory. IEEE Trans. Circuits Syst.. vol. 35, pp. 1257-1272, Oct. 1988.
14. _, Cellular neural network: Applications,'' IEEE Trans. Circuits Syst., vol. 35. pp. 1273-1290. Oct. 1988.
15. L. O. Chua and P. Thiran, An analytic method for designing simple cellular neural networks, IEEE Trans. Circuits Syst., vol. 38, pp. 1332-1341, Nov. 1991.
16. K. R. Crounse, T. Roska, and L. O. Chua, Image halftoning with cellular neural networks. IEEE Tram. Circuits Syst. II, vol. 40. pp. 267-283, June 1993.
17. S. Paul, K. Huper, J. Nossek, and L. 0. Chua, Mapping nonlinear lattice equations onto cellular neural networks, IEEE Trans. Circuits Syst. I, vol. 40, pp. 196-203, Mar. 1993.
18. T. Kozek, T. Roska, and L. 0. Chua, Genetic algorithm for CNN template learning, IEEE Trans. Circuits Sysl. I, vol. 40, pp. 392-402, June 1993.
19. R. E. Kaiman and J. E. Bertram, Control system analysis and design via the 'second method' of Lyapunov Part I: Continuous-time systems. Trans. ASME, pp. 371 -393, June 1960.
20. L. Mirsky, An Introduction to Linear Algebra. New York: Dover, 1990.
21. C.-T. Chen, Linear System Theory and Design. Orlando, FL: Holt. Rinehart and Winston, 1984.