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。

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.

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针对BP算法的不足，使用混合学习算法训练网络，优化了网络参数。

Because of defects of BP algorithm, a hybrid learning algorithm is applied to train and optimize the network parameters.

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提出了一种基于下降的单纯形算法和模拟退火算法的混合优化算法用于反演地声参数。

In this paper, hybrid inversion algorithm based on DHS(Downhill Simplex) algorithm and FSA(Fast Simulated Annealing) is developed and applied to the problem of determining geoacoustic properties.

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