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为了提高语音分离算法的收敛速度以及分离性能，提出把拉普拉斯正态混合分布概率密度函数作为语音信号概率密度函数的估计，得到一个更加适合语音信号分离的激活函数，基于此函数提出一种快速语音分离算法.-In order to improve speech separation algorithm convergence speed and separation performance, raise the normal mixture distribution Laplace probability de
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自适应控制的简单程序，对于初学者有很大的帮助，大家共同学习，共同进步-In order to improve the adaptation property of the system by making the best of the direction of distribution of radar measurement errors, a optimum estimation algorithm for Kepler orbit is proposed, which yields a
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数值分析，对于普通的编程实现一定的算法有很大的帮组，这是本人作业，服役偶偶程序计一定的解释-In order to improve the adaptation property of the system by making the best of the direction of distribution of radar measurement errors, a optimum estimation algorithm for Kepler orbit is proposed, whic
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传统的短时谱估计语音增强算法通常假设语音谱分量相互独立，没有考虑语音谱分量间的相关性。针对这
一问题，该文提出一种新的基于多元Laplace分布模型的短时谱估计算法。首先，假设语音的离散余弦变换(DCT)
系数服从多元Laplace分布，以此利用谱分量间的相关性；在此基础上，利用多元随机矢量的高斯尺度混合模型表
示，推导得到语音DCT系数矢量的最小均方误差(MMSE)估计的解析表达式；并进一步推导了基于该分布模型的
语音存在概率，对最小均方误差估计子进行修正。实验结果表明，该算法
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分布估计算法，是一个数据包，能实现群的一般算法-Distribution estimation algorithm, which is a packet, can achieve the group of the general algorithm
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该算法基本思想是利用一组带有相应权值的随机样本即粒子去逼近PHD分布和基数分布，其意义在于不仅解决重积分计算没有闭式解的难题，而且在滤波过程中，PHD函数被一系列离散的带权值的样本近似，随着样本粒子数量的增加，PHDF接近于Bayes最优估计，而且不受模型线性和高斯假设的限制，可以适用于非线性非高斯的随机系统。-The algorithm the basic idea is to use a set of random sample with the corresponding weight n
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In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs.
This algorithm is a member of the
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continuous ACO.
In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs.
This algorithm
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正则模型的分布估计算法,测试函数为ZDT,DTLZ,WFG等，在加噪时，进行优化，有四种噪声水平(RM-MEDA.Regular model distribution estimation algorithm, test function for zdt, dtlz, wfg, etc., when adding noise, optimization, there are four kinds of noise level)
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