Gaussian model with the assumption of the normal distribution of plume concentration is mostly applied for the evaluation of local atmospheric pollution in the world.
在研究局部地区空气污染时，目前国内外应用较为广泛的仍是假设烟羽浓度为正态分布的高斯模式。
The Elitist model is utilized to ensure the stable convergence, and the Gaussian mutation operator is used to enhance the local search ability around every peak value.
采用最优保存策略和高斯变异算子，保证算法的稳定收敛和提高算法在每个峰值附近的局部搜索能力。
The traditional training methods of Gaussian Mixture Model(GMM) are sensitive to the initial model parameters, which often leads to a local optimal parameter in practice.
为了解决传统高斯混合模型（GMM）对初值敏感，在实际训练中极易得到局部最优参数的问题，提出了一种采用微粒群算法优化GMM参数的新方法。
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