Black body furnace temperature time series prediction model based on BPNN was built.
文章在神经网络的基础上，建立了黑体炉温度时序预测模型。
In that article, they provided some code based on Neil Schemenauer's Python module bpnn.
在那篇文章里，他们提供了一些基于Neil Schemenauer 的Python模块 bpnn 的代码。
The experiment results show that the BPNN fire detection system has good feature of robustness.
实验结果表明，该神经网络火灾探测系统具有良好的抗干扰能力。
This paper proposed solution method of bilateral multi-attribute negotiations based on BPNN-GA algorithm.
提出了基于BPNN - GA算法的双边多属性谈判求解方法。
The BPNN model of Bayesian regularization method was adopted to create the adaptivity and generalization of BPNN.
文中采用贝叶斯正则化与BP网络结合的方法，建立动态前馈校正模型。
Conclusion Because of the inspirit function's globaling and the number of the Hidden Layer'node uncertainty the BPNN was not done well.
结论反向传播网络在函数逼近方面差的原因是激励函数的全局性、隐层结点数目的不确定性。
The experimental results show that the network learning speed can be increased and the nonlinear errors of the sensors can be reduced by using BPNN.
实验结果表明采用BP神经网络可以提高网络收敛速度，大大减小传感器线性误差。
Constructed based on such an assumption, the BPNN might lead to pay heavy prices when used as a classifier even if the misclassifications took place rarely.
但基于这种假设构造的BPNN在进行分类时，即使是很少的失误也可能付出惨重的代价。
Dropping-melting points of the olefin modified by Brazil carnauba wax and Chinese wax were predicted using the modified BPNN. The predicting absolute deviations a.
采用该BPNN模型对巴西棕榈蜡和川蜡改性的石蜡滴熔点进行了预测，预测结果的误差为改性石蜡滴熔点预测的绝对误差A。
The constringency speed and generalization ability of optimized BPNN model are better than that of simple BPNN model, and the simulation result is close to reality.
遗传算法优化的BP神经网络在收敛速度和泛化能力上都较简单的BP神经网络要好，模拟结果更接近于真实值。
As complex information system usually contains large amount of attributes which can be used as the input variables of a model, it may bring about constructing a complicated BPNN.
考虑到大量的输入属性可能导致复杂的BPNN，研究了用启发式方法和并行穷举方法约简属性的方法。
To overcome the shortcomings of the sneak circuit analysis (SCA) based on BPNN, a binary neural network ensemble (BNNE) algorithm for the sneak circuit analysis (SCA) is proposed.
针对神经网络在潜在通路分析应用中的缺陷，提出了二进制神经网络集成(BNNE)算法。
But if single neuron PID controller designed in terms of BPNN Theory is adopted, the control effect is not satisfactory because the learning rate and speed of convergence are slow.
使用常规pid控制很难满足手指精确位置控制的要求，而采用依据BPNN原理设计成的常规单神经元pid控制器又因学习速率低，收敛速度慢，控制效果不能令人满意。
After the comparison of optimized BPNN model and simple BPNN model, the result shows that, it is completed feasible to use optimized BPNN model in cultivated land classification work.
将优化后的BP神经网络模型和简单的BP神经网络进行比较，实验结果表明，基于遗传算法优化的BP神经网络模型在耕地分等评价工作中的应用完全可行。
The prediction result demonstrates that SVM is more practicable and effective in the modeling of color matching for Textile Dyeing in comparison with numerical analysis and BPNN methods.
预测结果表明，在对织物染色配色建模过程中SVM比基于数值分析的计算机配色方法以及BP神经网络模型更实用，更有效。
The experimental results on the real industrial data demonstrate that the model based on SVM achieves good performance and has less prediction errors than those of BPNN and RBFNN models.
在实际工业数据上进行的实验结果表明，支持向量机模型对丙酮纯度具有良好的预测效果，性能优于反向传播神经网络和径向基网络模型。
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。
The stress field around the crack tip is simulated via the constructed Back-Propagation Neural Network (BPNN) whereby achieving the approximation of the stress field around the crack tip.
通过构造反向传播神经网络，对裂纹尖端的应力场进行模拟，进而实现对裂纹尖端应力场函数的逼近。
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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