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。
This research built a flight phase safety risk assessment model basing on Back Propagation(BP) neural network.
基于反向传播（BP）神经网络，建立了民用航空航段安全风险评估模型。
This article introduces a predictive model of Artificial Neural network of red tide biology density and environment factors by use of the back propagation (BP) network.
本文利用人工神经网络中的BP网络，建立赤潮生物密度与环境因子的人工神经网络的预报模型。
To enhance the validity of evaluation, based on ANN(artificial neural network), a comprehensive evaluation model of the safety of road traffic based on BP(back propagation) neural network was built.
为了提高评价的准确性，采用人工神经网络技术，建立了基于BP神经网络的道路交通安全综合评价模型。
A model for urban road network traffic congestion forecast based on probe vehicle technology, fuzzy logic judgement and back-propagation (BP) neural network was proposed.
提出了一种新的基于移动检测技术、神经网络和模糊判断方法的城市路网动态交通拥挤预测模型。
Combined Genetic Algorithms (ga) and back-propagation neural network (BP), an optimized GA-BP model was established to predict phosphorus content. Some data were chosen to train the network model.
结合遗传算法(GA)和误差反馈型神经网络(BP)，建立了优化的GA - BP神经网络预测模型，预测转炉炼钢过程钢液终点磷含量。
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。
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。
A method to predict the wood radial thermal conductivity based on back propagation (BP) neural network model which has non-linear relation highly was proposed.
利用神经网络所具有的输入-输出之间的高度非线性映射关系，给出一种利用BP神经网络模型预测木材径向导热系数的方法。
The trend part of the data can be fitted with BP (back propagation) neural network and the random part is processed by a normal ARMA (auto regressive moving average) model.
采用BP网络对不平稳时间序列进行数据拟合，处理趋势部分，利用ARMA模型处理随机部分。
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。
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。
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