Title :
A one-layer recurrent neural network for constrained single-ratio linear fractional programming
Author :
Liu, Qingshan ; Wang, Jun
Author_Institution :
Sch. of Autom., Southeast Univ., Nanjing, China
Abstract :
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.
Keywords :
constraint handling; mathematical programming; recurrent neural nets; box bound constraints; constrained single ratio linear fractional programming; linear equality; one layer recurrent neural network; single ration linear fractional programming problems; Artificial neural networks; Convergence; Linear programming; Programming; Quadratic programming; Recurrent neural networks;
Conference_Titel :
Circuits and Systems (ISCAS), 2011 IEEE International Symposium on
Conference_Location :
Rio de Janeiro
Print_ISBN :
978-1-4244-9473-6
Electronic_ISBN :
0271-4302
DOI :
10.1109/ISCAS.2011.5937759