Solution of Linear Programming Problems using a Neural Network with Non-Linear Feedback
Alternative metrics PlumXhttp://hdl.handle.net/11012/37226
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This paper presents a recurrent neural circuit for solving linear programming problems. The objective is to minimize a linear cost function subject to linear constraints. The proposed circuit employs non-linear feedback, in the form of unipolar comparators, to introduce transcendental terms in the energy function ensuring fast convergence to the solution. The proof of validity of the energy function is also provided. The hardware complexity of the proposed circuit compares favorably with other proposed circuits for the same task. PSPICE simulation results are presented for a chosen optimization problem and are found to agree with the algebraic solution. Hardware test results for a 2–variable problem further serve to strengthen the proposed theory.
KeywordsLinear Programming, Dynamical Systems, Neural Networks, Feedback Systems, Non-Linear Feedback.
Document typePeer reviewed
Document versionFinal PDF
SourceRadioengineering. 2012, vol. 21, č. 4, s. 1171-1177. ISSN 1210-2512
- 2012/4