TY - GEN

T1 - A one-layer dual recurrent neural network with a heaviside step activation function for linear programming with its linear assignment application

AU - Liu, Qingshan

AU - Wang, Jun

PY - 2011

Y1 - 2011

N2 - This paper presents a one-layer recurrent neural network for solving linear programming problems. The proposed neural network is guaranteed to be globally convergent in finite time to the optimal solutions under a mild condition on a derived lower bound of a single gain parameter. The number of neurons in the neural network is the same as the number of decision variables of the dual optimization problem. Compared with the existing neural networks for linear programming, the proposed neural network has salient features such as finite-time convergence and lower model complexity. Specifically, the proposed neural network is tailored for solving the linear assignment problem with simulation results to demonstrate the effectiveness and characteristics of the proposed neural network.

AB - This paper presents a one-layer recurrent neural network for solving linear programming problems. The proposed neural network is guaranteed to be globally convergent in finite time to the optimal solutions under a mild condition on a derived lower bound of a single gain parameter. The number of neurons in the neural network is the same as the number of decision variables of the dual optimization problem. Compared with the existing neural networks for linear programming, the proposed neural network has salient features such as finite-time convergence and lower model complexity. Specifically, the proposed neural network is tailored for solving the linear assignment problem with simulation results to demonstrate the effectiveness and characteristics of the proposed neural network.

KW - global convergence in finite time

KW - linear assignment problem

KW - linear programming

KW - Recurrent neural networks

UR - http://www.scopus.com/inward/record.url?scp=79959349867&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-21738-8_33

DO - 10.1007/978-3-642-21738-8_33

M3 - Conference contribution

AN - SCOPUS:79959349867

SN - 9783642217371

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 253

EP - 260

BT - Artificial Neural Networks and Machine Learning, ICANN 2011 - 21st International Conference on Artificial Neural Networks, Proceedings

T2 - 21st International Conference on Artificial Neural Networks, ICANN 2011

Y2 - 14 June 2011 through 17 June 2011

ER -