A one-layer recurrent neural network for constrained single-ratio linear fractional programming

In this paper, a one-layer recurrent neural network is presented for solving single-ration linear fractional programming problems subject to linear equality and box bound constraints. The convergence condition is derived to guarantee the solution optimality to the fractional programming problems if the design parameters in the neural network are larger than the derived lower bounds. Two numerical examples with simulation results show that the proposed neural network is efficient and accurate for solving constrained linear fractional programming problems.

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