染色体长度 meaning in Chinese
chromosome length
Examples
- The distances are additive along the length of the chromosome .
其距离值就是沿着染色体长度方向的累加。 - Finding the centromer index depends on finding then centromere and the length of then chromosome
着丝粒指数的求解依赖于着丝粒定位和染色体长度。 - Apart from no . 1 to 3 chromosome , the lengths of which exceed 1 . 00um , the lengths of all the others are within l . ooum , so the type of the new species is microchromosome
除了1 - 3号染色体的长度超过1 . 00 m外，其它染色体长度均在1 . ooum以内，该新种为小染色体，与其它十字花科植物的染色体较小的特征相一致。 - To reduce the makespan of variety and small batch job shop scheduling problem , a new approach using double genetic algorithms is proposed , and based on processing sequence of sub - lots , a novel encoding scheme for the variable chromosome length of lot splitting scheduling is presented . theoretically , the best optimization solution of overall situation can be achieved from the proposed algorithms
对在本文中所采用的遗传算法的相关算子进行了阐述;针对中小批量的多工艺加工计划调度问题,以优化生产周期为目标,提出了基于双遗传算法的多工艺加工计划调度算法,同时提出了一种基于工件批次加工顺序的变染色体长度的编码方法。 - 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
针对模糊聚类算法不适应复杂环境的问题,提出了一种新的动态进化聚类算法,克服了传统模糊聚类建模算法须事先确定规则数的缺陷.通过改进的遗传策略来优化染色体长度,实现对聚类个数进行全局寻优;利用fcm算法加快聚类中心参数的收敛;并引入免疫系统的记忆功能和疫苗接种机理,使算法能快速稳定地收敛到最优解.利用这种高效的动态聚类算法辨识模糊模型,可同时得到合适的模糊规则数和准确的前提参数,将其应用于控制过程可获得高精度的非线性模糊模型