Based on the powerful nonlinear reflection and training function of artificial neural networks, the model of BP neural network for foundation piles integrity testing is put forward.
利用人工神经网络强大的非线性映射能力和学习训练功能，提出了基于BP网络的基桩完整性检测模型。
This paper deals with the structural health detection using measured frequency response functions (FRFs) as input data toa back propagation (BP) artificial neural networks (ANNs).
研究将实测结构频率响应函数作为反向传递人工神经网络的输入数据，用来进行结构健康检测。
Based on BP network, the example of nonlinear regression by artificial neural networks is given.
以BP网络为例给出了基于人工神经网络的非线性回归实例分析。
This paper puts forward the ways of reliability prediction based on artificial neural networks, builds the three layers BP neural networks model, and presents its arithmetic in detail.
本文提出了基于人工神经网络的可靠性预测方法，建立了用于数控机床可靠性预测的三层BP神经网络模型，给出了具体的算法。
The training model of test simulation for car of inverted pendulum based on BP algorithm of artificial neural networks (ANN) is a BP network that has 4-input and 3-layer structure.
基于人工神经网络BP算法的倒立摆小车实验仿真训练模型，其倒立摆BP网络为4输入3层结构。
In this paper we have improved the BP algorithm in the processes of applying BP algorithm of artificial neural networks for the annual average sediment concentration in a watershed by multi-factors.
本文在用人工神经网络BP模型对流域年均含沙量进行多因素建模过程中，对BP算法进行了改进。
In many kinds of artificial neural networks, BP neural nets is one of the most pioneer and common models, it is successfully applied to equipment fault diagnosis.
在若干神经网络模型中，BP网络模型是人们认识最早、应用最广泛的一种，它也是在设备故障诊断领域应用最成功的一种神经网络模型。
The BP algorithm of Artificial Neural Networks and lots of experimental samples were used in training the BP model which succeeded in diagnosing four kinds of operational status.
应用神经网络的误差反向传播算法(BP)和大量的实测数据样本训练出了能在线诊断四种加工状态的BP模型并成功地诊断了实际加工状态。
The BP algorithm of Artificial Neural Networks and lots of experimental samples were used in training the BP model which succeeded in diagnosing four kinds of operational status.
应用神经网络的误差反向传播算法(BP)和大量的实测数据样本训练出了能在线诊断四种加工状态的BP模型并成功地诊断了实际加工状态。
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