非適應性免疫 的英文怎麼說

中文拼音 [fēishìyīngxìngmiǎnyì]
非適應性免疫 英文
nonadaptive immunity
  • 非 : Ⅰ名詞1 （錯誤） mistake; wrong; errors 2 （指非洲） short for africa 3 （姓氏） a surname Ⅱ動詞1 ...
  • 適 : 形容詞1 （適合） fit; suitable; proper 2 （恰好） right; opportune 3 （舒服） comfortable; well Ⅱ...
  • 應 : 應動詞1 （回答） answer; respond to; echo 2 （滿足要求） comply with; grant 3 （順應; 適應） suit...
  • 性 : Ⅰ名詞1 （性格） nature; character; disposition 2 （性能; 性質） property; quality 3 （性別） sex ...
  • 免 : Ⅰ動詞1 （去掉;除掉） dismiss; relieve; remove 2 （避免） avoid; escape; avert 3 （免去） excuse s...
  • 疫 : 名詞（瘟疫） epidemic disease; pestilence
  • 適應性 : adaptability; flexibility
  • 適應 : suit; adapt; get with it; fit
  1. Dr casanova thinks hse is the first example of a disease that was thought to be purely infectious but which has turned out to be purely monogenic ? that is, under the control of a single gene

    與適應性免疫相反，先天免疫與生俱來的，而非後天由於人們接觸致病菌而形成抗體所獲得的。
  2. A novel dynamic evolutionary clustering algorithm ( deca ) is proposed in this paper to overcome the shortcomings of fuzzy modeling method based on general clustering algorithms that fuzzy rule number should be determined beforehand. deca searches for the optimal cluster number by using the improved genetic techniques to optimize string lengths of chromosomes ; at the same time, the convergence of clustering center parameters is expedited with the help of fuzzy c - means ( fcm ) algorithm. moreover, by introducing memory function and vaccine inoculation mechanism of immune system, at the same time, deca can converge to the optimal solution rapidly and stably. the proper fuzzy rule number and exact premise parameters are obtained simultaneously when using this efficient deca to identify fuzzy models. the effectiveness of the proposed fuzzy modeling method based on deca is demonstrated by simulation examples, and the accurate non - linear fuzzy models can be obtained when the method is applied to the thermal processes

    針對模糊聚類演算法不適應復雜環境的問題，提出了一種新的動態進化聚類演算法，克服了傳統模糊聚類建模演算法須事先確定規則數的缺陷.通過改進的遺傳策略來優化染色體長度，實現對聚類個數進行全局尋優;利用fcm演算法加快聚類中心參數的收斂;並引入免疫系統的記憶功能和疫苗接種機理，使演算法能快速穩定地收斂到最優解.利用這種高效的動態聚類演算法辨識模糊模型，可同時得到合適的模糊規則數和準確的前提參數，將其應用於控制過程可獲得高精度的非線性模糊模型
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