Loukianov, AlexanderSánchez-Torres, Juan D.Sánchez-Camperos, Edgar2016-04-262016-04-262014-12-09Sánchez-Torres, J.D.; Sánchez, E.N.; Loukianov, A.G., "A discontinuous recurrent neural network with predefined time convergence for solution of linear programming,"Swarm Intelligence (SIS), 2014 IEEE Symposium on, Orlando, USA, Dec. 2014, pp.1,5, 9-12.INSPEC: 14855601http://hdl.handle.net/11117/3331The aim of this paper is to introduce a new recurrent neural network to solve linear programming. The main characteristic of the proposed scheme is its design based on the predefined-time stability. The predefined-time stability is a stronger form of finite-time stability which allows the a priori definition of a convergence time that does not depend on the network initial state. The network structure is based on the Karush-Kuhn-Tucker (KKT) conditions and the KKT multipliers are proposed as sliding mode control inputs. This selection yields to an one-layer recurrent neural network in which the only parameter to be tuned is the desired convergence time. With this features, the network can be easily scaled from a small to a higher dimension problem. The simulation of a simple example shows the feasibility of the current approach.engRecurrent Neural NetworksLinear ProgrammingPredefined-time Stabilityinfo:eu-repo/semantics/conferencePaper