Finite time dual neural networks with a tunable activation function for solving quadratic programming problems and its application
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| Published in | Neurocomputing (Amsterdam) Vol. 143; pp. 80 - 89 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Amsterdam
Elsevier
02.11.2014
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0925-2312 |
| DOI | 10.1016/j.neucom.2014.06.018 |
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| Author | Miao, Peng Shen, Yanjun Xia, Xiaohua |
|---|---|
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| Keywords | Additive noise Recurrent neural nets Recurrent neural networks Stability Finite-time stability Computer simulation Quadratic programming problem Lyapunov method Tunable activation function Conveyor belt Neural network Quadratic programming Flexibility Modeling Delay Activation function Optimal solution Problem solving Time interval Belt conveyor Lyapunov function Quadratic function |
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| SubjectTerms | Applied sciences Artificial intelligence Computer science; control theory; systems Connectionism. Neural networks Continuous conveyors: mechanical, hydraulic, pneumatic, etc Exact sciences and technology Flows in networks. Combinatorial problems Material handling, hoisting. Storage. Packaging Mathematical programming Operational research and scientific management Operational research. Management science |
| Title | Finite time dual neural networks with a tunable activation function for solving quadratic programming problems and its application |
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