General Recurrent Neural Network for Solving Generalized Linear Matrix Equation
نویسندگان
چکیده
منابع مشابه
A Recurrent Neural Network Model for Solving Linear Semidefinite Programming
In this paper we solve a wide rang of Semidefinite Programming (SDP) Problem by using Recurrent Neural Networks (RNNs). SDP is an important numerical tool for analysis and synthesis in systems and control theory. First we reformulate the problem to a linear programming problem, second we reformulate it to a first order system of ordinary differential equations. Then a recurrent neural network...
متن کاملSolving LR fuzzy linear matrix equation†
In this paper, the fuzzy matrix equation $Awidetilde{X}B=widetilde{C}$ in which $A,B$ are $n times n$crisp matrices respectively and $widetilde{C}$ is an $n times n$ arbitrary LR fuzzy numbers matrix, is investigated. A new numerical procedure for calculating the fuzzy solution is designed and a sufficient condition for the existence of strong fuzzy solution is derived. Some examples are ...
متن کاملSolving Linear Semi-Infinite Programming Problems Using Recurrent Neural Networks
Linear semi-infinite programming problem is an important class of optimization problems which deals with infinite constraints. In this paper, to solve this problem, we combine a discretization method and a neural network method. By a simple discretization of the infinite constraints,we convert the linear semi-infinite programming problem into linear programming problem. Then, we use...
متن کاملGlobal conjugate gradient method for solving large general Sylvester matrix equation
In this paper, an iterative method is proposed for solving large general Sylvester matrix equation $AXB+CXD = E$, where $A in R^{ntimes n}$ , $C in R^{ntimes n}$ , $B in R^{stimes s}$ and $D in R^{stimes s}$ are given matrices and $X in R^{stimes s}$ is the unknown matrix. We present a global conjugate gradient (GL-CG) algo- rithm for solving linear system of equations with multiple right-han...
متن کاملA Recurrent Neural Network Model for solving CCR Model in Data Envelopment Analysis
In this paper, we present a recurrent neural network model for solving CCR Model in Data Envelopment Analysis (DEA). The proposed neural network model is derived from an unconstrained minimization problem. In the theoretical aspect, it is shown that the proposed neural network is stable in the sense of Lyapunov and globally convergent to the optimal solution of CCR model. The proposed model has...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Complexity
سال: 2017
ISSN: 1076-2787,1099-0526
DOI: 10.1155/2017/9063762