TY - GEN
T1 - A one-layer recurrent neural network for constrained single-ratio linear fractional programming
AU - Liu, Qingshan
AU - Wang, Jun
PY - 2011
Y1 - 2011
N2 - 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. © 2011 IEEE.
AB - 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. © 2011 IEEE.
UR - http://www.scopus.com/inward/record.url?scp=79960886650&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-79960886650&origin=recordpage
U2 - 10.1109/ISCAS.2011.5937759
DO - 10.1109/ISCAS.2011.5937759
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781424494736
SP - 1089
EP - 1092
BT - Proceedings - IEEE International Symposium on Circuits and Systems
T2 - 2011 IEEE International Symposium of Circuits and Systems (ISCAS 2011)
Y2 - 15 May 2011 through 18 May 2011
ER -