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temporary solution group
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  相似匹配句对
     HTK solution (H ) group;
     HTK液 (H)组 ;
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     THE IDEAL SOLUTION OF GROUP DECISION
     群体决策的理想解
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     Group C.
     实验动物随机分为3组,每组9只。
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     solution.
     溶液进行对照。
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     . group's.
     而且实验组的增量显著高于控制组的增量。
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Owning to immune system's powerful information processing capabilities,such as feature extraction,pattern recognition,learning,memory,and its distributive nature,immune algorithm(IA)has emerged,and gradually been applied to many engineering practices.However,there are limitations such as the lack of convergence efficiency in the existing immune algorithm models.This problem is caused by the evaluation form,the proliferation and suppression of antibodies,and the use of the memory library.Thus the paper improves...

Owning to immune system's powerful information processing capabilities,such as feature extraction,pattern recognition,learning,memory,and its distributive nature,immune algorithm(IA)has emerged,and gradually been applied to many engineering practices.However,there are limitations such as the lack of convergence efficiency in the existing immune algorithm models.This problem is caused by the evaluation form,the proliferation and suppression of antibodies,and the use of the memory library.Thus the paper improves the existing immune algorithm,and then proposes dynamic evaluation based immune algorithm.By introducing the temporary solution group,the evaluation,proliferation and suppression of antibodies take more times in a dynamic way,which updates the solution group and improves the convergence speed of the algorithm in later calculation.Besides,the memory library is used from the beginning to the end,which also helps to accelerate the convergence speed.Last,the proposed algorithm is described with Markov chain,and the convergence of the algorithm is proved.

由于免疫系统在特征抽取、模式识别、学习、记忆等方面强大的信息处理功能以及其自身的分布式特性,所以免疫算法出现,并逐渐应用于许多工程实际。然而,现有的免疫算法模型存在不少缺陷,缺乏有效的收敛特性。问题主要出在抗体的评价、促进和抑制以及记忆库的使用上。该文提出了基于动态评价的免疫算法,在算法中引入临时解集,对抗体进行多次、动态的评价、促进和抑制,这大大提高了算法的收敛速度,并有效防止算法陷入局部最优。另外,记忆库自始至终一直被利用,有利于算法快速收敛。最后,用马尔可夫链描述了该算法,并证明了算法的收敛性。

 
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