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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仿真实验研究表明：这种新型混合优化算法，计算简单，收敛速度快，初值鲁棒性好。

The simulative experiment demonstrates that this new kind of mixed optimal algorithm is simple in computation, rapid in convergent speed and good in robustness of initial value.

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利用混合光学双稳混沌序列的伪随机性和初值敏感性，提出一种用于数字水印中的水印图像置乱算法。

Using the sensitivity to the initial conditions and parameters of mixed-optical bistable chaotic sequence, we have a new scrambling algorithm about watermarking image.

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