This paper discusses backpropagation neural network model and BP algorithm to determine market response functions.
探讨了利用反向传播神经网络和BP算法确定市场响应函数的方法。
The experimental research on the networks have been made by backpropagation arithmetic and some optimal parameters are obtained.
利用反向传播算法对网络进行试探性训练研究，得到了最佳参数。
Using backpropagation neural network based on Levenberg-Marquardt algorithm, the universal characteristics of engine were investigated.
采用基于L - M算法的BP神经网络对某发动机万有特性进行研究。
Experiment results show that feed-forward backpropagation network achieves the best performance, which reduces average error rate by54.4%.
实验结果表明前馈后向传播网络的性能最好，与基准模型比较平均错误率下降54.4%。
The results of image edge detection show that the algorithm has better convergence properties than the conventional backpropagation learning technique.
经图像边缘检测应用结果表明，该算法对于加快网络学习的收敛性有着显著成效。
To eliminate the shortcoming of standard backpropagation algorithm, some modified BP algorithms in the MATLAB's neural networks toolbox are given in the paper.
针对标准BP算法存在的缺陷，本文给出了基于MATLAB语言的BP神经网络几种改进的算法。
This paper develops a novel backpropagation networks based adaptive multistep prediction technique for a class of nonlinear dynamical systems, and the prediction mechanism is analyzed.
本文针对一类非线性动态系统，提出了一种新的基于后向回归网络的自适应多步预测方法，并对基于神经网络的自适应预测机理进行了分析。
The learning of Backpropagation Neural Network (BPNN) aimed at lowering the classification error, usually assuming that all the samples had equal price when misclassifications were made.
传统的反向传播神经网络(BPNN)学习以分类错误最小为目标，通常假定在分类错误时所有样本的代价完全相同。
This paper presents a kind of software faults prediction model based on artificial neural network and the structure of the feed-forward multi-layer network with backpropagation learning algorithm.
该文介绍了一种基于人工神经网络的软件失效预测模型，给出了基于反向传播算法的多层前向网络的网络结构。
Aim To eliminate the influences of backlash nonlinear characteristics generally existing in servo systems, a nonlinear compensation method using backpropagation neural networks (BPNN) is presented.
目的为了消除普遍存在于伺服系统中的间隙非线性的影响，提出一种利用B P神经网络进行非线性补偿的方法。
Aim To eliminate the influences of backlash nonlinear characteristics generally existing in servo systems, a nonlinear compensation method using backpropagation neural networks (BPNN) is presented.
目的为了消除普遍存在于伺服系统中的间隙非线性的影响，提出一种利用B P神经网络进行非线性补偿的方法。
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