親合抗體群 的英文怎麼說
中文拼音 [qīngěkàngtǐqún]
親合抗體群
英文
avid antibody population-
The characteristics of quantum computing and the mechanism of immune evolution are analyzed and discussed. inspired by the mechanism in which immune cell can gradually accomplish affinity maturation during the self - evolution process, a immune evolutionary algorithm based on quantum computing ( mqea ) is proposed. the algorithm can find out optimal solution by the mechanism in which antibody can be clone selected, memory cells can be produced, similar antibodies can be suppressed and immune cell can be expressed as quantum bit ( q - bit ). it not only can maintain quite nicely the population diversity than the classical evolutionary algorithm, but also can help to accelerate the convergence speed and converge to the global optimal solution rapidly. the convergence of the mqea is proved and its superiority is shown by some simulation experiments in this paper
分析和探討了量子計算的特點及免疫進化機制，並結合免疫系統的動力學模型和免疫細胞在自我進化中的親和度成熟機理，提出了一種基於量子計算的免疫進化演算法.該演算法使用量子比特表達染色體，通過免疫克隆、記憶細胞產生和抗體相似性抑制等進化機制可最終找出最優解，它比傳統的量子進化演算法具有更好的種群多樣性、更快的收斂速度和全局尋優能力.在此不僅從理論上證明了該演算法的收斂，而且通過模擬實驗表明了該演算法的優越性Abstract : the characteristics of quantum computing and the mechanism of immune evolution are analyzed and discussed. inspired by the mechanism in which immune cell can gradually accomplish affinity maturation during the self - evolution process, a immune evolutionary algorithm based on quantum computing ( mqea ) is proposed. the algorithm can find out optimal solution by the mechanism in which antibody can be clone selected, memory cells can be produced, similar antibodies can be suppressed and immune cell can be expressed as quantum bit ( q - bit ). it not only can maintain quite nicely the population diversity than the classical evolutionary algorithm, but also can help to accelerate the convergence speed and converge to the global optimal solution rapidly. the convergence of the mqea is proved and its superiority is shown by some simulation experiments in this paper
文摘:分析和探討了量子計算的特點及免疫進化機制，並結合免疫系統的動力學模型和免疫細胞在自我進化中的親和度成熟機理，提出了一種基於量子計算的免疫進化演算法.該演算法使用量子比特表達染色體，通過免疫克隆、記憶細胞產生和抗體相似性抑制等進化機制可最終找出最優解，它比傳統的量子進化演算法具有更好的種群多樣性、更快的收斂速度和全局尋優能力.在此不僅從理論上證明了該演算法的收斂，而且通過模擬實驗表明了該演算法的優越性
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