Neural network design for behavioral model generation with shape preserving properties

2000 IEEE/ACM International Workshop on Behavioral Modeling and Simulation

Volumn , Issue , 2000, Pages 97-102

  Mikulchenko, Oleg ^a   Mayaram, Kartikeya ^a  

^a OREGON STATE UNIVERSITY   (United States)

Author keywords

Behavioral modeling; Device modeling; Micro flow sensor modeling; MOS transistor modeling; Neural networks; Simulation

Indexed keywords

MICRO-FLOW SENSORS;

COMPUTATIONAL METHODS; COMPUTER SIMULATION; LARGE SCALE SYSTEMS; SENSORS;

NEURAL NETWORKS;

EID: 0034593170     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/BMAS.2000.888371     Document Type: Conference Paper

References (26)

1. H. Chang, E. Charbon, U. Choudhury, and A. Demir, A Top-Down, Constraint-Driven Design Methodology for Analog Integrated Circuits. Kluwer Academic Pub, 1997.
2. P. Meijer, "Fast and smooth highly nonlinear table models for device modeling," IEEE Transactions on Circuits and Systems, vol. 37, pp. 335-346, March 1990.
3. J. Peters and U. Reif, "Analysis of algorithms generalizing B-spline subdivision," vol. 35, pp. 728-748, Apr. 1998.
4. C. de Boor and A. Ron, "On multivariate polynomial interpolation," Constructive Approximation, vol. 6, pp. 287-302, 1990.
5. L. M. Kocic and G. V. Milovanovic, "Shape preserving approximations by polynomials and splines," Computers and Mathematics with Applications, vol. 33, pp. 59-97, June 1997.
6. Q. J. Zhang and K. C. Gupta, Neural Networks for Rf and Microwave Design. Artech House, 2000.
7. P. Burrascano and M. Mongiardo, "A review of artificial neural networks applications in microwave CAD," International Journal on RF and Microwave Computer-Aided Engineering, vol. 9, no, 3, 1999.
8. G. Creech and J. Zurada, "Neural network modeling of semiconductor material and device characteristics," International Journal on RF and Microwave Computer-Aided Engineering, vol. 9, no. 3, 1999.
9. S. Lawrence, C. L. Giles, and A. Tsoi, "Lessons in neural network training: Overfitting may be harder than expected," in Proceedings of the Fourteenth National Conference on Artificial Intelligence, AAAI-97, pp. 540-545, Menlo Park, California: AAAI Press, 1997.
10. R. D. Reed and R. J. Marks, Neural smithing: supervised learning in feedforward artificial neural networks. The MIT Press, 1999.
11. A. Lapedes and R. Farber, "How neural nets work," in Neural Information Processing Systems (D. Z. Anderson, ed.), (New York), pp. 442-456, (Denver 1987), American Institute of Physics, 1988.
12. Y. Takahashi, "Generalization and approximation capabilities of multilayer networks," Neural Computation, vol. 5, no. 1, pp. 132-139, 1993.
13. D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning. Reading: Addison-Wesley, 1989.
14. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, "Learning representations by back-propagating errors," Nature, vol. 323, pp. 533-536, 1986.
15. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, "Learning internal representations by error propagation," in Parallel Distributed Processing (D. E. Rumelhart and J. L. McClelland, eds.), vol. 1, ch. 8, pp. 318-362, Cambridge: MIT Press, 1986.
16. P. E. Gill, W. Murray, and M. H. Wright, Practical Optimizations. Academic Press, 1981.
17. D. F. Shanno, "Conjugate gradient methods with inexact searches," Math. Oper. Res., vol. 3, no. 3, pp. 244-256, 1978.
18. K. Levenberg, "A method for the solution of certain nonlinear probelms in least squares," Quart. Appl. Math., vol. 2, pp. 164-168, 1944.
19. D. W. Marquardt, "An algorithm for least-squares estimation of nonlinear parameters," Journal of the Society for Industrial and Applied Mathematics, vol. 11, pp. 431-441, June 1963.
20. H. Demuth and M. Beale, "Neural network toolbox." For use with MATLAB. User's guide. Version 3.0.
21. J. J. E. Dennis and R. B. Schnabel, Numerical methods for unconstrained optimization and nonlinear equations, vol. 16 of Classics in Applied Mathematics. Philadelphia, PA: Society for Industrial and Applied Mathematics (SIAM), 1996. Corrected reprint of the 1983 original.
22. D. Montgomery, Design and Analysis of Experiments. John Wiley and Sons, New York, NY, USA, 1991.
23. H. Niederreiter and C. Xing, "Quasi-random points and global function fields," in Finite Fields and Applications (S. Cohen and H. Niederreiter, eds.), London Math. Soc. LN Series 233, pp. 269-296, 1996.
24. P. Bratley and B. L. Fox, "Implementing Sobol's quasirandom sequence generator," ACM Trans. Math. Softw., vol. 14, pp. 88-100, Mar. 1988.
25. P. Ko, J. Huang, Z. Liu, and C. Hu, "BSIM3 for analog and digital circuit simulation," in IEEE Symp. on VLSI Technology CAD, pp. 400-429, 1993.
26. O. Mikulchenko, A. Rasmussen, and K. Mayaram, "A neural network based macromodel for microflow sensors," Proc. of MSM2000, pp. 540-543, 2000.