提出了一种基于混合因子分析的分布估计算法。
The paper describes an approach based on estimation of distribution algorithms.
提出了一种基于混合因子分析的分布估计算法。
Estimation of distribution algorithms (EDAs) is a new meta-heuristic algorithm.
因此，分布估计算法的核心在于估计解空间的概率分布。
Therefore, the core of EDAs is to estimate the probability distribution of the solution space.
分布估计算法由于其较强的理论基础已成为进化计算研究的新热点。
Estimation of Distribution algorithms (EDAs) are new evolutionary algorithms based on probabilistic model and have become a new focus in the field of evolutionary computation.
连续域分布估计算法普遍采用高斯概率模型，假设变量服从高斯分布。
Estimation of distribution algorithms in continuous domains is based on such assumption that the variables subject to Gauss distribution.
该算法较好地克服了传统分布估计算法（EDA）在计算时间受限制时收敛可靠性不高的弊病不足。
The traditional Estimation of Distribution Algorithms (EDA) is limited by its inferior convergence reliability under limited calculation time.
本文针对组合优化问题，基于分布估计算法主要开展了如下研究：针对多维背包问题，设计了一种混合分布估计算法。
Aiming at combinatorial optimization problems, this paper carries out the following main research work based on EDA. A hybrid EDA is proposed to solve the multidimensional knapsack problems (MKP).
相对于传统的概率分布估计算法，并行的概率分布估计算法在解决连续函数优化及实时优化问题时能提供极大程度的效率提高。
In contrast to traditional estimate distribution algorithm, parallel EDAs has greatly improved the efficiency when optimizing continuous functions and real time questions.
通过对多目标优化方法研究现状的分析，针对多目标优化问题的特点提出一种基于联合正态分布的求解多目标优化问题的分布估计算法。
By analyzing the research status of Multi-Objective optimization, an Estimation of distribution Algorithm for Multi-Objective Problem based on joint normal distribution is proposed.
在这类特殊的多传感器系统中，本文通过矩阵运算消除相关估计方差，得到了最优分布式融合估计算法。
For this special multisensor system, distributed optimal fusion algorithm is received by avoiding computing correlated estimation covariance based on the matrix operation.
比较了目前分布式视频系统的各种码率估计算法性能，并提出了改进的码率估计算法。
With a comparison on a few of rate estimation algorithms, an improved methods resided in the decoder is proposed in this paper.
所以，我们需要一种初始的估计机制来计算路径空间中的区域的不同分布，从而以此为根据来选取最适合区域具体分布的DDM算法。
So the initial estimation mechanism is needed to compute the type of distributions of the extents in the routing space to build an adaptive DDM.
基于多传感器单模型动态系统的多尺度估计理论，研究了不同尺度上拥有不同统计特性的多尺度融合算法及多尺度分布式融合估计算法。
Basing multiscale estimation theory of multi-sensors and single-model of dynamic system, the multiscale fuse algorithm and multiscale distribute fuse algorithm were studied respectively.
分布式算法通过其边界协调方程来修正边界节点估计值，从而保证了计算的精度。
The DSE method modified the results of the boundary buses by the coordinate function, thus it ensures the calculating precision generally.
数字仿真表明，分布式联合、估计算法在各种性能指标上都优于单站跟踪算法。
The Monto Carlo simulation shows that the distributed fusion algorithm performs much better than the local estimation algorithm in every function index and achieves the expected effec…
数字仿真表明，分布式联合、估计算法在各种性能指标上都优于单站跟踪算法。
The Monto Carlo simulation shows that the distributed fusion algorithm performs much better than the local estimation algorithm in every function index and achieves the expected…
在算法的状态估计阶段，采用混合系统粒子滤波和二元估计算法同时估计对象系统故障演化模型混合状态和未知参数的后验分布。
For state estimation of hybrid system with unknown transition probabilities, an adaptive estimation algorithm is proposed based on Monte Carlo particle filtering.
仿真结果表明基于协作的分布式估计算法的估计精度比Kalman估计算法更高，估计误差小于0。
Simulations indicate that distributed estimation based cooperation has higher estimation accuracy than the Kalman estimation algorithm with an estimation accuracy of less than 0.05.
绝对偏差最小法是一种适合于存在离群点时的稳健估计算法，可以克服最小二乘法仅在误差为正态分布时才有效的缺点。
Therefore, least absolute deviation, which is more robust than least squares especially for Gaussian noise, is selected to reduce the random error.
绝对偏差最小法是一种适合于存在离群点时的稳健估计算法，可以克服最小二乘法仅在误差为正态分布时才有效的缺点。
Therefore, least absolute deviation, which is more robust than least squares especially for Gaussian noise, is selected to reduce the random error.
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