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。
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。
Combining wavelet analysis and neural network characteristics, the error back propagation wavelet neural network based structure and algorithm to ship roll time series prediction are given.
结合小波分析和神经网络的特点，建立了应用于船舶横摇运动时间序列预报的误差反传小波神经网络结构并给出了算法。
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。
A neural network model with dynamical compensating capability is analyzed. During the training of this network model, we apply the principle of dynamic error back-propagation.
本文分析了一种动态补偿神经网络模型，模型的训练利用反向传播原理实现。
By means of BP (error back propagation) artificial nerve network, with data from alarm, weather and engineering documents, microwave hop performance analysis and forecast model is established.
利用神经网络的误差反向传播算法(BP算法)，结合告警、天气和工程设计几方面的数据资料建立了微波中继段告警分析预测模型。
The model and learning algorithms of BP( Error Back Propagation)network, which is widely applied, is recommended, and RBF( Radial B asis Function)is simply recommended contrastively.
本文首先介绍了神经网络中应用最为成熟广泛的BP网络的模型及其学习算法，并简单对比介绍了RBF网络。
Method:Artificial neural network based on error back propagation was used.
方法：应用人工神经网络误差反向传播模型进行含量测定。
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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