International Journal of Neural SystemsImproved neural networks for linear and nonlinear programming - Chen, Shanblatt - 1992Chen J., Shanblatt M. And Maa C., Improved Neural Network for Linear and Nonlinear Programming, International Journal of Neural Systems, no. 2, pp. 331- 339, 1992....
Burkard, R. E., Dollani, H. & Thach, P. T. Linear approximations in a dynamic programming approach for the uncapacitated single-source minimum concave cost network flow problem in acyclic networks.J. Glob. Optim.19, 121–139 (2001). ArticleMathSciNetMATHGoogle Scholar Xi, X., Huang, X...
Noun1.linear programming- a mathematical technique used in economics; finds the maximum or minimum of linear functions in many variables subject to constraints applied math,applied mathematics- the branches of mathematics that are involved in the study of the physical or biological or sociological worl...
Recurrent neural networkWeighting problemIn this paper, a representation of a recurrent neural network to solve fuzzy non-linear programming (FNLP) problems is given. The motivation of the paper is to design a new effective one-layer structure recurrent neural network model for solving the FNLP. ...
Solution of linear programming prob- lems using a neural network with non-linear feedback. Radioengineering, 21(4):1171, 2012.S.A. Rahman, M.S. Ansari and A.A. Moinuddin, Solution of linear programming problems using a neural network with non-linear feedback, Radio Engineering, 21(2012)...
Recurrent neural networksIn this brief, based on the method of penalty functions, a recurrent neural network (NN) modeled by means of a differential inclusion is proposed for solving the bilevel linear programming problem (BLPP). Compared with the existing NNs for BLPP, the model has the ...
By means of new theorems on duality, a sort of recurrent neural network for solving linear programming problems is given, which can be realized easily by circuits. The algorithm's exponentially asymptotic stability in the whole is proven. It makes the neural computing approach for linear programmin...
2.3Trained Neural Networks and MILP Already trained neural networks and mixed integer linear programming have been brought successfully together in the past. Note, that this work is focussing on the direct optimization of the network weights and its parameters from training data with a close connectio...
(Torres et al.2008). Other approaches use linear programming (LP) or integer linear programming (ILP) (Maria and Fahmy1974, Chottera and Jullien1982a,b), neural network (Burger et al.2012), or genetic algorithms (Pedrino et al.2013, Harvey and Marshall1996) to generate filters that can...
In this paper linear neural network was applied to adaptive noise cancellation technology,and the neural network was trained by least mean square (LMS) algorithm. 针对有源滤波器谐波检测实时精度高的要求,将线性神经网络应用于自适应噪声对消技术,采用最小均方(least mean square,LMS)误差算法对神经网络进...