运用梯度最陡下降法，推导出能量函数曲线演化方程，并应用于图像分割。
Using gradient-descent methods the energy function is minimized and a curve evolution equation is obtained to segment the image.
然后提出了一种新的LMS算法——改进的最陡下降法，该算法正是求最佳权矢量的一个简单而有效的算法。
Then modified steepest descent method which is a new LMS algorithm of adaptive filters is presented , This method is a simple and effective algorithm to solve optimum coefficient .
然后系统阐述了基本维纳滤波原理和自适应滤波器的基本结构模型，接着在此基础上结合最陡下降法引出LMS算法。
Then we explain basic theory of wiener filter and basic structure model of adaptive filter, and combine the method of steepest descent to deduce the LMS.
在二维情况下，忽略热扩散效应，利用汉克尔积分变换法和最陡下降法，求解热扩散方程和波动方程，得出了位移的解析表达式。
The thermal equation and wave equation are solved by the methods of Hankel transform and steepest descent, and the general expression of displacement is obtained.
给出了该自适应网络的结构，在此基础上给出了网络权值的修正算法，即综合最陡下降法和最小二乘法得到的一种混合学习算法。
The structure of ANFIS is proposed. Then a mixed learning arithmetic based on back promulgate and least-square arithmetic is presented to modify the network parameters.
它对一定时间间隔内两次采样得到的图象进行运算，用最陡梯度下降法直接迭代出图象位移的估计值。
The algorithm which utilizes the Steepest Descent Method can give the estimation of displacement between two frames of image directly.
它对一定时间间隔内两次采样得到的图象进行运算，用最陡梯度下降法直接迭代出图象位移的估计值。
The algorithm which utilizes the Steepest Descent Method can give the estimation of displacement between two frames of image directly.
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