Current-Mode Neural Networks For Solving Nonlinear Programming Problems

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Author AI Naima, Fawzi en_US
Available date 2009-11-25T13:04:01Z en_US
Publication Date 2000 en_US
Citation Engineering Journal of Qatar University, 2000, Vol. 13, Pages 223-244. en_US
URI http://hdl.handle.net/10576/7864 en_US
Abstract In this paper, neural networks for online solution of linear and nonlinear programming problems are presented. Current mode circuits are used in the design of networks. Transimpedance techniques based current conveyors are used for implementing the neurons. Two types of neurons are used in these networks: one being an integrator and the other being used as constraint. Many methods are suggested in this paper to implement networks weights by using current mode circuits such as Operational Transconductance Amplifiers. Network parameters are explicitly computed based upon problem specifications, to cause the network to converge to an equilibrium that represents a solution. Simulation results using Electronic workbench EDA version S.Oa-1996 shows the optimization solutions are obtained almost in real time, acceptable if they are compared with theoretical values, and are stable. en_US
Language en en_US
Publisher Qatar University en_US
Subject Engineering: Electrical Engineering en_US
Title Current-Mode Neural Networks For Solving Nonlinear Programming Problems en_US
Type Article en_US
Pagination 223-244 en_US
Volume Number 13 en_US


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