An efficient recurrent neural network model for solving fuzzy non-linear programming problems
| contributor author | امین منصوری | en |
| contributor author | سهراب عفتی | en |
| contributor author | محمد اسحاق نژاد | en |
| contributor author | Amin Mansoori | fa |
| contributor author | Sohrab Effati | fa |
| contributor author | mohammad eshaghnezhad | fa |
| date accessioned | 2020-06-06T13:31:01Z | |
| date available | 2020-06-06T13:31:01Z | |
| date issued | 2016 | |
| identifier uri | https://libsearch.um.ac.ir:443/fum/handle/fum/3358129?show=full | |
| description 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 | en |
| language | English | |
| title | An efficient recurrent neural network model for solving fuzzy non-linear programming problems | en |
| type | Journal Paper | |
| contenttype | External Fulltext | |
| subject keywords | Fuzzy non-linear programming problems · Bi-objective problem · Weighting problem · Recurrent neural network · Globally stable in the sense of Lyapunov · Globally convergent | en |
| journal title | Applied Intelligence | fa |
| pages | 308-327 | |
| journal volume | 46 | |
| journal issue | 2 | |
| identifier link | https://profdoc.um.ac.ir/paper-abstract-1058183.html | |
| identifier articleid | 1058183 |
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