Name
Title | ||
|---|---|---|
An efficient recurrent neural network model for solving fuzzy non-linear programming problems. |
Abstract | ||
|---|---|---|
In 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. Here, we change a fuzzy non-linear programming problem to a bi-objective problem. Furthermore, the bi-objective problem is reduced to a weighting problem and then the Lagrangian dual and the Karush-Kuhn-Tucker (KKT) optimality conditions are constructed. The simulation results on numerical examples are discussed to demonstrate the performance of our proposed approach. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s10489-016-0837-4 | Appl. Intell. |
Keywords | Field | DocType |
Fuzzy non-linear programming problems,Bi-objective problem,Weighting problem,Recurrent neural network,Globally stable in the sense of Lyapunov,Globally convergent | Mathematical optimization,Lagrangian,Computer science,Fuzzy logic,Nonlinear programming,Recurrent neural network,Artificial intelligence,A-weighting,Karush–Kuhn–Tucker conditions,Machine learning | Journal |
Volume | Issue | ISSN |
46 | 2 | 0924-669X |
Citations | PageRank | References |
12 | 0.58 | 28 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amin Mansoori | 1 | 58 | 5.31 |
| Effati Sohrab | 2 | 276 | 30.31 |
| Mohammad Eshaghnezhad | 3 | 54 | 3.91 |