Neural Network to Solve Concave Games
The issue on neural network method to solve concave games is concerned. Combined with variational inequality, Ky Fan inequality, and projection equation, concave games are transformed into a neural network model. On the basis of the Lyapunov stable theory, some stability results are also given. Fina...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
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| Series: | International Journal of Computer Games Technology |
| Online Access: | http://dx.doi.org/10.1155/2014/249721 |
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| _version_ | 1832567914684743680 |
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| author | Zixin Liu Nengfa Wang |
| author_facet | Zixin Liu Nengfa Wang |
| author_sort | Zixin Liu |
| collection | DOAJ |
| description | The issue on neural network method to solve concave games is concerned. Combined with variational inequality, Ky Fan inequality, and projection equation, concave
games are transformed into a neural network model. On the basis of the Lyapunov stable theory, some stability results are also given. Finally, two classic games’ simulation results are given to
illustrate the theoretical results. |
| format | Article |
| id | doaj-art-0c89d08bb1ef450994177079fbf47815 |
| institution | Kabale University |
| issn | 1687-7047 1687-7055 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Computer Games Technology |
| spelling | doaj-art-0c89d08bb1ef450994177079fbf478152025-02-03T01:00:09ZengWileyInternational Journal of Computer Games Technology1687-70471687-70552014-01-01201410.1155/2014/249721249721Neural Network to Solve Concave GamesZixin Liu0Nengfa Wang1College of Computer Science and Information, GuiZhou University, Guiyang 550025, ChinaDepartment of Mathematics and Statistics, Guizhou University of Finance and Economics, Guiyang 550004, ChinaThe issue on neural network method to solve concave games is concerned. Combined with variational inequality, Ky Fan inequality, and projection equation, concave games are transformed into a neural network model. On the basis of the Lyapunov stable theory, some stability results are also given. Finally, two classic games’ simulation results are given to illustrate the theoretical results.http://dx.doi.org/10.1155/2014/249721 |
| spellingShingle | Zixin Liu Nengfa Wang Neural Network to Solve Concave Games International Journal of Computer Games Technology |
| title | Neural Network to Solve Concave Games |
| title_full | Neural Network to Solve Concave Games |
| title_fullStr | Neural Network to Solve Concave Games |
| title_full_unstemmed | Neural Network to Solve Concave Games |
| title_short | Neural Network to Solve Concave Games |
| title_sort | neural network to solve concave games |
| url | http://dx.doi.org/10.1155/2014/249721 |
| work_keys_str_mv | AT zixinliu neuralnetworktosolveconcavegames AT nengfawang neuralnetworktosolveconcavegames |