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。
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。
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。
The following paper constructs a artificial neural network - named water quantity predicting model, using automatically adapting and step-self-changing back propagation method(ABPM).
采用自适应变步长的后向传播算法（ABPM）构建了一个人工神经网络用水量预测模型。
Objective To study the application of back propagation artificial neural network model in prediction for incidence of hemorrhagic fever with renal syndrome.
目的探讨反馈人工神经网络模型预测肾综合征出血热发病率的应用前景。
A typical artificial neural network model-back-propagation model was presented for prediction on the soil liquefaction type based on the physical parameters of soils under earthquake.
本文根据人工神经网络的一典型模型—反向传播模型，以及地震荷载下的各项土的物理—力学参数，建立了土液化类型的神经网络数学模型。
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。
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。
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。
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 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。
应用推荐