Title :
Analysis and design of a recurrent neural network for linear programming
Author_Institution :
Dept. of Ind. Technol., North Dakota Univ., Grand Forks, ND, USA
fDate :
9/1/1993 12:00:00 AM
Abstract :
Linear programming is an important tool for system optimization and modelling. This paper presents a recurrent neural network with a time-varying threshold vector for solving linear programming problems. The proposed recurrent neural network is proven to be asymptotically stable in the large and capable of generating optimal solutions to linear programming problems. An op-amp based analog circuit design for realizing the recurrent neural network is described. The asymptotic properties of the proposed recurrent neural network for linear programming are analyzed. A detailed example is also presented to demonstrate the performance and operating characteristics of the recurrent neural network
Keywords :
analogue processing circuits; linear programming; network analysis; network synthesis; operational amplifiers; recurrent neural nets; stability; asymptotic properties; asymptotically stable; linear programming; op-amp based analog circuit design; operating characteristics; optimal solutions; recurrent neural network; time-varying threshold vector; Analog circuits; Equations; Large-scale systems; Linear programming; Neural networks; Neurons; Operational amplifiers; Real time systems; Recurrent neural networks; Vectors;
Journal_Title :
Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions on
