DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jeong, Il-Kwon | - |
dc.contributor.author | Choi, Changkyu | - |
dc.contributor.author | Shin, Jin-Ho | - |
dc.contributor.author | Lee, Ju-Jang | - |
dc.date.accessioned | 2009-01-12T09:11:02Z | - |
dc.date.available | 2009-01-12T09:11:02Z | - |
dc.date.issued | 1996 | - |
dc.identifier.citation | Evolutionary Computation, 1995., IEEE International Conference on, Volume: 1, On page(s): 306-311 | en |
dc.identifier.isbn | 0-7803-2759-4 | - |
dc.identifier.uri | http://hdl.handle.net/10203/8305 | - |
dc.description.abstract | Genetic algorithms are getting more popular nowadays because of their simplicity and robustness. Genetic algorithms are global search techniques for optimizations and many other problems. A feed-forward neural network that is widely used in central applications usually learns by back propagation algorithm (BP). However, when there exist certain constraints, BP cannot be applied. We apply a genetic algorithm to such a case. To improve hill-climbing capability and speed up the convergence, we propose a modified genetic algorithm (MGA). The validity and efficiency of the proposed algorithm. MGA are shown by various simulation examples of system identification and nonlinear system control such as cart-pole systems and robot manipulators | en |
dc.language.iso | en_US | en |
dc.publisher | IEEE | en |
dc.subject | Genentic algorithm | en |
dc.subject | neurocontroller | en |
dc.title | A Modified Genetic Algorithm for Neurocontrollers | en |
dc.type | Article | en |
dc.identifier.doi | 10.1109/ICEC.1995.489164 | - |
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