- 更多网络例句与输入数据误差相关的网络例句 [注：此内容来源于网络，仅供参考]
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The automatic tracking function of the ARPA is out of order.
ARPA 的自动跟踪功能坏了，我们只能手动输入数据，误差太大。
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The results indicate that the ANN structure and the training sample have some impact on the prediction precision. The real time measured power as input will improve the precision of 30 min ahead prediction, however will decrease the precision of 1h ahead prediction. The results which using the atmospheric data at all different heights as input have a higher accuracy when compared with the results using hub height data only. The designed ANN can forecast the error band.
研究结果表明，神经网络的结构和输入样本对预测结果有一定的影响；实测功率数据作为输入可以提高提前量为30 min的预测精度，而对提前量为1 h的预测精度会降低；把不同高度的数据都作为神经网络的输入比只采用轮毂高度数据的预测精度高；设计的神经网络能够对误差带进行预测。
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In fuzzy division of input space of the model, the division references consider not only the space distance of input sample data, but also the rule output error, thus defining"generalized sample distance", which makes model structure better.
在模型输入空间的模糊划分中，不单只考虑输入样本数据的"空间距离"，同时将规则输出误差作为输入空间划分的另一依据，由此定义"广义样本距离"，使模型结构更加合理。
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Aimed at the characteristic of multiple types of faults possibly happened in nuclear power plant, large scale of training sample, and requirement of quick and accurate diagnosing, after the analysis of large sample number generated by large fault type exceeds limitation of 64K paragraph in DOS and large number of local minmum in error surface, measurements of same format of data file, Win95/NT operating system platform, resetting weight learning rate, dynamic training set in quick learning algorithm and improvement on quick learning algorithm using homotopy method which can avoid local minmum points in error surface have been adopted to ensure quickly and effective process of the course of neural network's training and testing.
针对核动力装置可能发生的故障种类多，训练样本规模大，故障诊断需要快速准确的特点等，分析了样本多和故障种类多产生的训练样本量超出DOS段大小和网络误差曲面上局部极小值多的情况，采取了使用相同格式数据文件、Win95/NT操作系统平台，对快速学习算法采用重置神经网络权值学习率和动态训练集、并采用能够有效克服网络误差曲面上局部极小点的同伦方法对学习算法进行改进等措施保证神经网络训练测试过程的快速、有效地进行；针对装置发生的故障须快速、准确诊断的需要，分析了故障的产生对装置参数变化的影响及操纵员对故障诊断的基础，在核动力装置发生故障时参数曲线的变化量与正常运行时参数曲线的变化量存在明显差异的基础上，提出采用参数曲线的变化量作为神经网络的输入，并围绕参数变化量的方法采用二次曲线拟合滤波求变化量和阈值技术来保证神经网络得到精确的装置参数变化量，从而得到准确的诊断结果。
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So we designed a adaptive synchronizing controller based on hereinbefore hardware environment: first a small magnitude reference signal r is outputted to system through the signal card (to ensure that the vibrating system works in a linear state), and this signal is sent to the moving coil of vibrator through the power amplifier, so vibration is produced through electromagnetic induction. Secondly the vibration signal can feedback to the data acquiring card in the servo system through the acceleration sensor on the Vibroseis reactor M〓 and the base-plate M〓, then the computer can get the current vibrating state y〓 of the coil of vibrator according to the feedback information from the data acquiring card, and give a real time comparing between the current state y〓 and the reference output y〓 of the set-in reference model with current reference input being r , then regulates the correlative controlling parameters according to the error e〓=y〓-y〓 till y〓→y〓, finally normal signal sweeping begins with a certain phase fixed. Meanwhile a synchronization signal for seismic signal record is sent to seismograph from synchronization signal outputting component in the Vibroseis system to perform the controlling process of synchronization of sweeping phases.
为此，我们基于以上的硬件环境设计了一个自适应同步控制器：首先通过信号发生卡对系统输出小幅度的参考信号r(从而保证振动系统工作处于线性状态)，信号通过前置放大器、功率放大器等送到激震器动圈，并通过电磁感应产生振动，振动信号通过可控震源激震器反应块M〓和基板M〓上的加速度传感器反馈给伺服系统中的数据采集卡，工控计算机根据采集卡的反馈信息，获取当前激震器动圈的振动状态y〓，并实时地将该状态与内置的参考模型在当前参考输入r下的参考输出y〓进行辨识，再将两者输出误差e〓＝y〓-y〓对系统的有关控制参数进行调整，直至y〓→y〓，最后在经过某一固定的相位后，开始信号的正常扫描过程，与此同时，由可控震源系统的同步信号输出部件向地震仪送出一地震信号记录同步信号，进而完成扫描相位同步控制过程。
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Fuzzy neural network is used in the three phase adaptive reclosure so that the reclose of permanent faults can be avoided. A multi-input fuzzy neural network is constructed and the algorithm of network is designed. Taking the virtual value of three phase voltage as the input with its scale changed, the network learning can be completed with convergence reached after the process of fuzzification, normalization and perspicuity, so it can disinguish transient faults from permanent faults.
为了避免重合闸重合于永久性故障，将模糊神经网络应用于三相自适应重合闸中，构造了一个多输入的模糊神经网络，设计了网络的算法，将三相端压有效值作为输入，经尺度变换、模糊化、归一化和清晰化，并利用梯度下降法修正误差，使得网络完成学习并最终收敛，在输入故障数据时能根据网络的输出结果准确地判别瞬时性故障与永久性故障。
- 更多网络解释与输入数据误差相关的网络解释 [注：此内容来源于网络，仅供参考]
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cross product：乘積
RSREG 建立二项式反应面 ( Response-Surface) 的回归模型AUTOREG 利用时间系列的数据导出回归模型 此法中各误差 (E rrors) 之间可以是* 输入数据可以是相关系数矩阵或是向量内乘积 (Cross Product) 的矩阵* 提供共线性 ( Collineari
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error in the input data：输入数据误差
error function 误差函数 | error in the input data 输入数据误差 | error law 误差律
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input error：输入数据误差
input device 输入装置 | input error 输入数据误差 | input flow 输入流
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input flow：输入流
input error 输入数据误差 | input flow 输入流 | input function 输入函数
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spur：毛刺
信号杂波比 (SNR) 是输入信号的均方根值功率与均方根值噪声功率的比值 (排除谐波失真),其大小以分贝 (dB) 表示,方程式(3)是信噪比的定义:频域分析显示数据转换器的非线性误差会造成谐波失真,这些失真将以"毛刺"(spur) 形式出现在信号谐