TY - JOUR
T1 - A deterministic annealing neural network for convex programming
AU - Wang, Jun
PY - 1994
Y1 - 1994
N2 - A recurrent neural network, called a deterministic annealing neural network, is proposed for solving convex programming problems. The proposed deterministic annealing neural network is shown to be capable of generating optimal solutions to convex programming problems. The conditions for asymptotic stability, solution feasibility, and solution optimality are derived. The design methodology for determining design parameters is discussed. Three detailed illustrative examples are also presented to demonstrate the functional and operational characteristics of the deterministic annealing neural network in solving linear and quadratic programs. © 1994.
AB - A recurrent neural network, called a deterministic annealing neural network, is proposed for solving convex programming problems. The proposed deterministic annealing neural network is shown to be capable of generating optimal solutions to convex programming problems. The conditions for asymptotic stability, solution feasibility, and solution optimality are derived. The design methodology for determining design parameters is discussed. Three detailed illustrative examples are also presented to demonstrate the functional and operational characteristics of the deterministic annealing neural network in solving linear and quadratic programs. © 1994.
KW - Convergence analysis
KW - Convex programming
KW - Recurrent neural network
UR - http://www.scopus.com/inward/record.url?scp=0028320687&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-0028320687&origin=recordpage
U2 - 10.1016/0893-6080(94)90041-8
DO - 10.1016/0893-6080(94)90041-8
M3 - 21_Publication in refereed journal
VL - 7
SP - 629
EP - 641
JO - Neural Networks
JF - Neural Networks
SN - 0893-6080
IS - 4
ER -