With the back-propagation algorithm in hand, we can turn to our puzzle of identifying the language of source code samples.
在掌握了反向传播算法后，可以来看我们的识别源代码样本语言的难题。
where j varies over all the output nodes that receive input from n. Moreover, the basic outline of a back-propagation algorithm runs like this.
这里每个从n接收输入的输出节点j都不同。关于反向传播算法的基本情况大致如此。
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。
The I/O relationship of back propagation algorithm (BP algorithm) for Feed-Forward Multi-layered Neural Network is a mapping relationship, which can resolve the above nonlinear problem.
多层前向神经网络的误差逆传播算法（简称BP算法）的输入输出关系实际上是一种映射关系，适于解决上述非线性映射问题。
High nonlinear problem which is often met in Chemical engineering, taking the study of tray leakage mode as example, is treated by adopted BP (back propagation) algorithm in artificial neural network.
针对化工中经常遇到的高度非线性问题，以塔板研究中的泄漏模型为例，采用人工神经网络中的BP(反向传播)算法进行处理。
Then the algorithm of the Tutoring positive signal propagation and error back-propagation of two processes, and in which the signal value deduction, deduction for error analysis and elaboration.
然后对本授导算法中的信号正向传播与误差反向传播两个过程，以及其中的信号值推演、误差值推演进行了分析和阐述。
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。
The simple improved back propagation algorithm of the SNC is liable to real-time and on-line control.
单神经元控制器(snc)结构简单，改进的反向传播算法适合实时在线控制。
The potential utility of feed forward artificial neural network using the back propagation algorithm (BP-ANN), in interpreting pyrogram data from traditional Chinese medicine was discussed.
将以误差反向传播为训练算法的前馈式人工神经网络（BP- ANN）首次用于中草药的裂解气相色谱谱图解析。
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 BP (back propagation) model for excitation characteristics of transformer, a modified BP model is presented and the corresponding algorithm is also deduced.
在变压器励磁特性的BP模型基础上，提出了一种修正的BP模型，并推导了相应的算法。
The mathematic frame of the neural network and error back propagation algorithm were introduced.
介绍了小波神经网络的数学框架及其误差反向学习算法。
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。
Next, an Improved Back Propagation(IBP)algorithm is proposed, considering the drawbacks of the standard Back Propagation (BP) in the neural network theory.
接着，作者对神经网络理论中的标准反向传播算法BP作了改进并提出了IBP算法。
The paper proposes a new kind of algorithm for uyghur character separation: forth propagation character separation algorithm and back propagation character separation algorithm.
提出了两种新的维文分字算法：正向分字算法和反向分字算法。
Back propagation (BP) algorithm is often used for the weights training of neural network, but the convergence speed of BP algorithm is slow.
反向传播(BP)算法常常用于神经网络的权值训练中，但是BP算法收敛慢。
In this paper, the back propagation algorithm of a multilayer feedforward neural network was defined as BP algorithm?
利用前向多层神经网络的反向传播算法，即BP算法。
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。
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。
Secondly, the hybrid training algorithm is proposed on the base of the Error Back-propagation algorithm's disadvantage analysis in the paper.
其次，从误差反传算法在预测中存在的问题入手，提出一种混合训练算法。
The new model was based on the weight adjustments of error back propagation of BP algorithm and the weight modification using particle swarm optimization (PSO).
提出一种基于粒子群算法优化BP网络的权值调整新方法。
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。
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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