A Discrete-time Neural Network for Solving Convex Optimization Problem in Support Vector Machine
This paper proposes a discrete-time neural network model to solve the convex optimization problem deduced by a positive-kernel-based support vector machine ( SVM) . First，the projection equations are constructed through the Karush-Kuhn-Tucker ( KKT) conditions and projection theory so that there exi...
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| Format: | Article |
| Language: | zho |
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Harbin University of Science and Technology Publications
2018-08-01
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| Series: | Journal of Harbin University of Science and Technology |
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| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1571 |
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| _version_ | 1849229061301207040 |
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| author | LIU Feng-qiu ZHANG Hong-xu |
| author_facet | LIU Feng-qiu ZHANG Hong-xu |
| author_sort | LIU Feng-qiu |
| collection | DOAJ |
| description | This paper proposes a discrete-time neural network model to solve the convex optimization problem deduced by a positive-kernel-based support vector machine ( SVM) . First，the projection equations are constructed through the Karush-Kuhn-Tucker ( KKT) conditions and projection theory so that there exists a one-to-one correspondence between the solution of projection equations and the optimal solution of optimization problem，and then a discrete-time neural network was constructed by projection equations. Second，the obtained theoretical results indicate that the equilibrium point of the proposed neural network corresponds to the optimal solution of the optimization problem，and the proposed neural network is globally exponentially convergent. Compared with some continuous neural networks， the architecture of proposed neural network is simple， which decreases the computational complexity. Finally，some classification problems and benchmarking data sets are used in the experiment. The numeral results show the efficiency of the proposed neural network for solving the optimization problem in SVM. |
| format | Article |
| id | doaj-art-eff9eb2c2eb34ac2b7265c6d03d90af6 |
| institution | Kabale University |
| issn | 1007-2683 |
| language | zho |
| publishDate | 2018-08-01 |
| publisher | Harbin University of Science and Technology Publications |
| record_format | Article |
| series | Journal of Harbin University of Science and Technology |
| spelling | doaj-art-eff9eb2c2eb34ac2b7265c6d03d90af62025-08-22T09:34:13ZzhoHarbin University of Science and Technology PublicationsJournal of Harbin University of Science and Technology1007-26832018-08-01230413313910.15938/j.jhust.2018.04.025A Discrete-time Neural Network for Solving Convex Optimization Problem in Support Vector MachineLIU Feng-qiu0ZHANG Hong-xu1School of Applied Sciences, Harbin University of Science and Technology, Harbin 150080, ChinaSchool of Applied Sciences, Harbin University of Science and Technology, Harbin 150080, ChinaThis paper proposes a discrete-time neural network model to solve the convex optimization problem deduced by a positive-kernel-based support vector machine ( SVM) . First，the projection equations are constructed through the Karush-Kuhn-Tucker ( KKT) conditions and projection theory so that there exists a one-to-one correspondence between the solution of projection equations and the optimal solution of optimization problem，and then a discrete-time neural network was constructed by projection equations. Second，the obtained theoretical results indicate that the equilibrium point of the proposed neural network corresponds to the optimal solution of the optimization problem，and the proposed neural network is globally exponentially convergent. Compared with some continuous neural networks， the architecture of proposed neural network is simple， which decreases the computational complexity. Finally，some classification problems and benchmarking data sets are used in the experiment. The numeral results show the efficiency of the proposed neural network for solving the optimization problem in SVM.https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1571discrete time neural networksupport vector machineconvex optimizationglobal exponential convergent |
| spellingShingle | LIU Feng-qiu ZHANG Hong-xu A Discrete-time Neural Network for Solving Convex Optimization Problem in Support Vector Machine Journal of Harbin University of Science and Technology discrete time neural network support vector machine convex optimization global exponential convergent |
| title | A Discrete-time Neural Network for Solving Convex Optimization Problem in Support Vector Machine |
| title_full | A Discrete-time Neural Network for Solving Convex Optimization Problem in Support Vector Machine |
| title_fullStr | A Discrete-time Neural Network for Solving Convex Optimization Problem in Support Vector Machine |
| title_full_unstemmed | A Discrete-time Neural Network for Solving Convex Optimization Problem in Support Vector Machine |
| title_short | A Discrete-time Neural Network for Solving Convex Optimization Problem in Support Vector Machine |
| title_sort | discrete time neural network for solving convex optimization problem in support vector machine |
| topic | discrete time neural network support vector machine convex optimization global exponential convergent |
| url | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1571 |
| work_keys_str_mv | AT liufengqiu adiscretetimeneuralnetworkforsolvingconvexoptimizationprobleminsupportvectormachine AT zhanghongxu adiscretetimeneuralnetworkforsolvingconvexoptimizationprobleminsupportvectormachine AT liufengqiu discretetimeneuralnetworkforsolvingconvexoptimizationprobleminsupportvectormachine AT zhanghongxu discretetimeneuralnetworkforsolvingconvexoptimizationprobleminsupportvectormachine |