Abstract
The problem to be considered is to create a circuit able to solve the system of linear equations
where D is a nonsingular n × n matrix of real constant coefficients, v ∈ R n is the vector of variables, and b ∈ R n the vector of real constant coefficients.
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References
Chua, L.O., “Stationary Principles and Potential Functions for Nonlinear Networks”, J. Franklin Inst, vol. 296, no. 2, pp. 91–114, Aug. 1973.
Chua, L.O. and Lin, G.-N., “Nonlinear Programming without Computation”, IEEE Trans, on Circuits Syst, vol. CAS-31, pp. 182–188, Feb. 1984.
Chua, L.O. and Lin, G.-N., “Errata to ‘Nonlinear Programming without Computation’”, IEEE Trans, on Circuits Syst., vol. CAS-32, p. 736, July 1985.
Hopfield, J.J. and Tank, D.W., “Simple ‘Neural’ Optimization Networks: An A/D Converter, Signal Decision Circuit and a Linear Programming Circuit.”, IEEE Trans, on Circuits Syst, vol. CAS-33, no. 5, pp. 533–541, May 1986.
Kennedy, M.P. and Chua, L.O., “Unifying the Tank and Hopfield Linear Programming Circuit and the Canonical Nonlinear Programming Circuit of Chua and Lin”, IEEE Trans, on Circuits Syst., vol. CAS-34, no. 2, pp. 210–214, Feb. 1987.
Minick, J.R., “Application of Neural Networks to Computer-Aided Design of Electronic Circuits: Solution of Linear Equations”, Undergraduate Fellow Honor Thesis, Department of Electrical Engineering, Texas A&M University, College Station, TX, April 1990.
Vemuri, V., “Artificial Neural Networks: An Introduction”, in Artificial Neural Networks: Theoretical Concepts, Computer Society Press Technology Series, pp. 1–7, 1988.
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Styblinski, M.A., Minick, J.R. (1994). Two Methods for Solving Linear Equations Using Neural Networks. In: Delgado-Frias, J.G., Moore, W.R. (eds) VLSI for Neural Networks and Artificial Intelligence. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-1331-9_22
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DOI: https://doi.org/10.1007/978-1-4899-1331-9_22
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