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...
First a Linear Programming formulation is considered for the satisfiability problem, in particular for the satisfaction of a Conjunctive Normal Form in the Propositional Calculus and the Simplex algorithm for solving the optimization problem. The use of Recurrent Neural Networks is then described for ...
A new learning algorithm for feedforward neural networks based on linear programming is introduced. This alternative to back-propagation gives faster and more reliable learning on reasonably sized examples. Extensions of the method for efficient (approximate) implementations in large networks are considered...
In this paper we study three different classes of neural network models for solving linear programming problems. We investigate the following characteristics of each model: model complexity, complexity of individual neurons, and accuracy of solutions. Simulation examples are given to illustrate the dynami...
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)...
摘要: A circuit for online solving of linear programming problems is presented. The circuit uses switched-capacitor techniques and is thus suitable for monolithic implementation. The connection of the proposed circuit to analogue neural networks is also outlined....
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 ...
In addition, the energy function of (3) is a quadratic convex function in a neighborhood of the optimal solutions, and thus the network can converge faster than that of (2). The projection technique is further used to construct neural network for solving extended linear programming problems ...
A combination of such perceptrons in a directed acyclic graph leads to a classic (e.g., fully connected) neural network. 2.2 Mixed Integer Linear Programming Linear programming (LP) is a method for the minimization (or maximization) of a linear objective function, subject to linear equality or...
We present a high-performance and efficiently simplified new neural network which improves the existing neural networks for solving general linear and quadratic programming problems. The network, having no need for parameter setting, results in a simple hardware requiring no analog multipliers, is shown...