Meanwhile, the back propagation learning algorithm is given based on BP.
文章还推导了基于BP的反传学习算法。
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。
The learning algorithm is BP (Back Propagation) algorithm.
学习算法为反向传播算法。
Disadvantages of the back propagation algorithm are discussed, and the improved methods based on dynamic learning constants and different activation functions are presented.
讨论了误差逆传播算法存在的缺陷，并针对其缺陷提出了动态调整学习因子与合理选取激发函数相结合的改进方案。
Based on the idea of standard back-propagation (BP) learning algorithm, an improved BP learning algorithm is presented.
在标准反向传播神经网络算法的基础上，提出了一种改进的反向传播神经网络算法。
This paper provided the deduction of an algorithm for artificial neural network - the error back-propagation learning algorithm and the procedure to carry it out on computer.
提供了人工神经网络的一种算法-误差反向传播算法的数学推导方法及上机实现步骤。
In this paper, making use of Kalman filtering, we derive a new back-propagation algorithm whose learning rate is computed by Riccati difference equation.
本文运用卡尔曼滤波原理，提出了一种新的神经网络学习算法。该算法的学习速度是由带时间参数的里卡蒂微分方程来确定的。
Concerned with the training process and accuracy, the LM algorithm is superior to conjugate gradient algorithm and a variable learning rate back propagation (BP) algorithm.
就训练次数与精确度而言，它明显优于共轭梯度法及变学习率的BP算法，适用于系统辨识。
A one-by-one learning algorithm for multi-layer neural network modelling is presented based on the back-propagation mechanism of network error.
基于误差反向传播的机制，针对连续制造过程的预测与控制，提出多层神经网络的逐个样本学习算法。
A one-by-one learning algorithm for multi-layer neural network modelling is presented based on the back-propagation mechanism of network error.
基于误差反向传播的机制，针对连续制造过程的预测与控制，提出多层神经网络的逐个样本学习算法。
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