Based on gradient algorithm and the fundamental approximation of feedforward network, a new supervised comprehensive training mechanism is put forward.
基于梯度算法和前馈网络所具有的普遍近似性质，提出了一种新的监督型多目标系统化训练机制。
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。
An adaptive gradient descent algorithm for training simplified internally recurrent networks (SIRN) is developed and a new method of reconciling nonlinear dynamic data based on SIRN is proposed.
研究了简化型内回归神经网络基于自适应梯度下降法的训练算法，并提出了一种基于简化型内回归神经网络的非线性动态数据校核新方法。
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