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ganmin银虫 (正式写手)
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现在一篇中文稿件看清样，要我仔细检查英文摘要， 大侠们帮忙看看把 Based on an evolutionary algorithm (EA) and a local search strategy——the structured nonlinear parameter optimization method (SNPOM), two hybrid parameter optimization algorithms for RBF neural networks are proposed. The first approach starts with a population of some random initial parameter values, and updates the population by selection, crossover, and replacement according to the fitness values obtained by the SNPOM. The second method runs the EA for a reasonable amount of generations, after which the SNPOM is used to locate the refined local optimum. The basic idea of the two hybrid algorithms is to find the optimal initial values for SNPOM using EA. It is shown by the simulation tests that the combination provides better results than either method alone (EA and SNPOM) or some other existing algorithms. |
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wpq113 
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ganmin(金币+3,VIP+0):Thanks 6-24 19:40
ganmin(金币+2,VIP+0):谢谢！ 6-24 20:49
ganmin(金币+1,VIP+0):Thanks 6-25 12:18
ganmin(金币+3,VIP+0):Thanks 6-24 19:40
ganmin(金币+2,VIP+0):谢谢！ 6-24 20:49
ganmin(金币+1,VIP+0):Thanks 6-25 12:18
| Based on an evolutionary algorithm (EA) and a local search strategy——the structured nonlinear parameter optimization method (SNPOM), two hybrid parameter optimization algorithms for RBF neural networks are proposed. The first approach starts with a population of some random initial parameter values, and updates the population by selection, crossover, and replacement according to the fitness values obtained by the SNPOM. The second method runs the EA for a reasonable amount of generations, after which the SNPOM is used to locate the refined local optimum. The basic idea of the two hybrid algorithms is to find the optimal initial values for SNPOM using EA. It is shown by the simulation tests that the combination method provides better results than either the single method (EA and SNPOM) or some other existing algorithms. |
2楼2009-06-24 17:43:03
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ganmin(金币+1,VIP+0):谢谢帮忙顶 6-24 19:39
ganmin(金币+1,VIP+0):谢谢帮忙顶 6-24 19:39
帮顶一下 |
3楼2009-06-24 18:30:15
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4楼2009-06-24 19:15:39
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6楼2009-06-24 19:55:54
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8楼2009-06-24 20:50:00
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