...反向传播神经网络 [gap=1721]sub-image segmentation, coefficients of variances, singular value decomposition, back-propagation neural networks ...
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1 P 神经网络 BP 神经网络(Back-propagation Neural Networks)是单 向传播的多层前向网络,网络除输入输出层外,还有一层 或多层的隐含层节点,同层节点中没有任何耦合。
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其种类甚 多，本文采用多层回馈式类神经网路中，最著名的 监督式倒传递类神经网路(Back-propagation Neural Networks, BPNNs)作为系统模拟工具。
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倒传递网路（Back-Propagation Neural Networks）系将感知器模式网路学习规则 广义化到多层（增加隐藏层），且于网路中采用非线性可微分转移函数，建立高 维度学习网路而得。
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fast back propagation neural networks 快速bp网络
a back propagation neural networks 反向传播神经网络
multi-layer back propagation neural networks 多层前馈神经网络
back-propagation artificial neural Networks 反向传播人工神经网络
back propagation arificial neural networks 反向逆传播人工神经网络
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。
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。
Regular back-propagation networks (BP) are fully connected globalized neural networks, it is usually difficult for them to approximate illbehaved systems, which exist in any application field.
常规的反向传播网络（BP）是一种内部呈完全联结的全局性网络，它对非平滑系统的学习能力较弱。
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