Neural network BP training algorithm based on gradient descend technique may lead to entrapment in local optimum so that the network inaccurately classifies input patterns.
基于梯度下降的神经网络训练算法易于陷入局部最小，从而使网络不能对输入模式进行准确分类。
The trackability limitation of current gradient algorithm is discussed. A new algorithm, named variable parameter gradient estimation algorithm with local polynomial approximation is proposed.
本文分析了梯度辨识算法跟踪时变系统的缺点，提出了一种新的基于局部多项式逼近的变参数梯度估计算法。
The traditional fuzzy C-means (FCM) algorithm is an optimization algorithm based on gradient descending. it is sensitive to the initial condition and liable to be trapped in a local minimum.
传统的模糊c -均值(FCM)聚类是一种基于梯度下降的优化算法，该方法对初始化较敏感，且易陷入局部极小。
The approaches based on ANN or gradient hill climb algorithm have limitations such as the function form and local optimum.
采用人工神经网或梯度爬山算法均存在对优化函数形式有限制及陷入局部最优等局限性。
First, a method to estimate the local image gradient, called the BEG (best estimation of gradient) algorithm was proposed to extract the texture orientation parameters.
首先提出了局部图像的梯度估计方法，称之为BEG(梯度的最佳估计)算法，用来提取图像的纹理方向特征参数。
Moreover, an improved conjugate gradient algorithm is used to train the network and to overcome the shortcoming of easily trapping into local minimum points.
网络训练时采用共轭梯度学习算法并对此算法进行了改进，有效的克服了梯度学习算法容易陷入局部极小的缺点。
Moreover, an improved conjugate gradient algorithm is used to train the network and to overcome the shortcoming of easily trapping into local minimum points.
网络训练时采用共轭梯度学习算法并对此算法进行了改进，有效的克服了梯度学习算法容易陷入局部极小的缺点。
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