1) LMK(Least Mean Kurtosis) criterion
最小平均峰度准则
2) maximum kurtosis criteria
最大峰度准则
1.
Based on maximum kurtosis criteria, a new blind identification and equalization algorithm is designed for linear system.
根据最大峰度准则设计了一种针对线性系统的盲辨识与盲均衡算法。
3) LMK
最小均值峰度
1.
Least mean kurtosis (LMK) based on QPSO, which can obtain signal error ratio less than LMK, is proposed to predict the self similar traffic.
使用QPSO(quantum-behaved particle swarm optimization)对预测自相似性网络流量的最小均值峰度(LMK)方法进行优化,能够获得较小的信噪比SNR-1(signal to noise ratio)。
4) minimum mean entropy difference criterion
最小均熵差准则
1.
As the development of minimum mean variance criterion,the minimum mean entropy difference criterion is introduced by means of data entropy and data entropy difference and is applied to data analysis and pattern cluster.
作为最小均方差准则的拓广,本文从数据熵、数据熵差引进最小均熵差准则,并应用于数据与模式聚类;给出基予最小均熵差准则的动态聚类算法与系统聚类算法,最后通过一个应用示例说明这一最小均熵差准则模式聚类的有效性与优越性。
5) least-mean-square-error criterion
最小均方差准则
6) minimum square mean
最小平方准则
补充资料：最小辐亮度与最小辐照度(见核爆炸火球)
最小辐亮度与最小辐照度(见核爆炸火球)
minimum-brightness and minimum-irradiance
zuixiao fuliangdu yu zuixiaofu乙haodu最小辐亮度与最小辐照度(minimum-brightness and而nimum一irradianee)见核爆炸火球。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条