The essence of back propagation networks is that make the change of weights become little by gradient descent method and finally attain the minimal error.
其实质是采用梯度下降法使权值的改变总是朝着误差变小的方向改进，最终达到最小误差。
In this model, back propagation algorithm based on forward networks was conducted to learn information of historical data and to train the network weights.
以人工神经网络的前馈型网络为基础结构，基于反向传播算法进行学习和训练来拟和证券价格指数的运动趋势。
Since these variables are characterized as nonlinearities time series data, Artificial Neural networks (ANN) will be employed using back propagation algorithm as learning algorithm.
由于这些变量具有非线性时间序列数据，用人工神经网络(ANN)将使用反向传播算法作为学习算法。
Feedforward networks use back propagation algorithm to train a multi-layer network. After training, the multi-layer network can fit the function in the data space very well.
前向网络利用反向传播算法训练多层网络，使训练后的网络较好地拟合样本空间中各点的函数值。
This paper deals with the structural health detection using measured frequency response functions (FRFs) as input data toa back propagation (BP) artificial neural networks (ANNs).
研究将实测结构频率响应函数作为反向传递人工神经网络的输入数据，用来进行结构健康检测。
There are a few training algorithms for parameter estimation of neural networks, in which Back Propagation(BP)algorithm is the typical algorithm for feed-forward multi-layer neural networks.
神经网络参数估计有许多训练算法，BP算法是前向多层神经网络的典型算法，但BP算法有时会陷入局部最小解。
Regular back-propagation networks (BP) are fully connected globalized neural networks, it is usually difficult for them to approximate illbehaved systems, which exist in any application field.
常规的反向传播网络（BP）是一种内部呈完全联结的全局性网络，它对非平滑系统的学习能力较弱。
Regular back-propagation networks (BP) are fully connected globalized neural networks, it is usually difficult for them to approximate illbehaved systems, which exist in any application field.
常规的反向传播网络（BP）是一种内部呈完全联结的全局性网络，它对非平滑系统的学习能力较弱。
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