Introduce the Error Back-Propagation algorithm.
介绍了误差逆传播算法。
After some research I have chosen a multilayer perceptron and standard back-propagation algorithm for training.
经过我选择了多层感知和标准的反向传播训练算法的研究。
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。
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。
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 introduces a new adaptive fuzzy system with Gauss fuzzier (GAF), optimizing the parameter based on the data from PID by back-propagation algorithm.
本文提出一种带有高斯模糊器的自适应模糊系统(GAF)，基于PID控制的成功经验数据，通过反向传播学习算法对其进行参数寻优。
This mapping relation is determined by training neural network with a back-propagation algorithm, which is utilized to estimate images at finer resolution from coarser versions.
使用反向传播算法训练神经网络，确定这种映射关系；根据该映射关系由低分辨力图像估计高分辨力图像。
This paper gave a brief review on neural nets and neural computers, neuron models, layered network structures, error back-propagation algorithm and its application to geophysical inversion.
本文简要介绍神经网络与神经计算机，神经元模型，层状网络结构，误差逆传播算法及其在地球物理反演中的应用。
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。
The learning algorithm is BP (Back Propagation) algorithm.
学习算法为反向传播算法。
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 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。
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。
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。
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。
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。
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。
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。
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。
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。
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