Based on the steepest descent method and the conjugate gradient method, a hybrid algorithm is proposed in this paper, and its global convergence is proved.
将最速下降法与共轭梯度法有机结合起来，构造出一种混合优化算法，并证明其全局收敛性。
The corresponding adaptive algorithm was derived based on the steepest descent method.
基于最陡下降方法，推导出了相应的自适应算法。
The learning method of hidden-output layer weights is the steepest descent method and the one of input-hidden layer weights is genetic algorithm(GA) .
网络隐层-输出层的权值采取最速下降法学习，输入层-隐层的权值采用遗传算法进行学习；
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。
Its learning rule is to use the steepest descent method, by back-propagation network to continuously adjust the weights and thresholds, so the network and the minimum sum of squared errors.
它的学习规则是使用最速下降法，通过反向传播来不断调整网络的权值和阈值，使网络的误差平方和最小。
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.
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。
应用推荐