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.
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.
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.
该系统的控制器采用模糊神经网络控制器，它的控制器参数采用模拟退火算法全局优化来对BP算法进行改进的混合方法。
The parameters of the fuzzy neural network controller are optimized by the mixed learning methods with BP algorithm and Simulated Annealing algorithm which improves BP algorithm.
算例表明，混合算法在参数反演计算中体现出良好的优化性能和很快的收敛速度，是一种新颖可行的参数反演方法。
The results of an example show that the hybrid approach is of strong ability to obtain global minima, and its performances are superior to those of single method.
算例表明，混合算法在参数反演计算中体现出良好的优化性能和很快的收敛速度，是一种新颖可行的参数反演方法。
The results of an example show that the hybrid approach is of strong ability to obtain global minima, and its performances are superior to those of single method.
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