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network output
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  网络输出
     In the study, a neural network with 14-15-1 structure was used, regarding 14 physic-chemical factors of the water as network input, average corroding rate of Q235 steel as network output.
     所用网络结构为14-15-1的形式,以试验用水的温度和水质成分等14种环境因素作为网络输入,以Q235钢在试验用水中的平均腐蚀速度作为网络输出.
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     The third, according to the aforementioned project, a hardware system is provided which includes the core chip-TMS320DSC21 (TI special DSP) and its peripheral circuits (clock and power supply), video input module, network output module, alarm module, and cradle head control module.
     接着以TI公司的专用DSP TMS320DSC21为核心,进行了嵌入式网络摄像机的硬件设计,硬件部分包括:DSC21核心及相关电路(时钟、电源和调试电路)、视频输入模块、视频数据网络输出模块、报警模块和云台控制模块。
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     The two ports negative resistance oscillating network is analyzed and an extremum-line model of microwave transistor oscillating network output impedance is developed.
     对双端口负阻振荡网络进行了理论分析,介绍了三种双端口负阻振荡网络的分析方法,在负阻振荡器参数优化问题上提出了微波晶体管负阻振荡网络输出阻抗的极值线模型,该模型把对反馈元件和谐振元件参数的二维搜索问题转化为一维搜索问题,所以使优化过程大大简化,缩短了电路设计时间。
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     Regarding to the simplified Euler Beam modal, this paper will make use of the finite element method to calculate the FRF, with considering the compress transaction of response data by using PCA as the input for optimizing the BP network model, and Euler Beam's four exponent modal frequency and modal damping as network output.
     本文就简单的Euler梁模型,采用有限元方法计算其频率响应函数(FRF),用主元分析方法(PCA)对频响数据进行压缩处理作为优化BP网络模型的输入,以Euler梁前四阶模态频率和模态阻尼作为网络输出
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     The network input is pile displacement, and the network output is unknown parameters.
     把桩体侧向位移作为神经网络的输入,而土体力学参数作为网络输出,对神经网络进行训练。
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  “network output”译为未确定词的双语例句
     The experimental results show that the influence of background light fluctuation can be eliminated effectively,and a desired linear relationship between the sensor input and the neural network output can be obtained.
     实验结果表明,该方法能有效地消除背景光的影响,在神经网络的输出端得到期望的线性输出。
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     A 4-layer modified feedforward neural network based on multi-factor input and network output by adding main factor linear relativity with hypo-factor nonlinear relativity,is the newest feasible method of field control of FeO content in sinter.
     采用改进后的4层前向神经网络,进行多因素输入建模,输出采用主因线性相关与次因非线性相关叠加,预报现场烧结矿FeO含量。
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     After 2159 times of training,the neural network output of the typhoon track-westward,northward and northwestward,is compared with thehistorical data.
     为适应台风路径预报,对BP网络进行了改进,应用改进后的BP网络,自动训练到2159次后,由神经网络输出的判定台风移向趋势──西进,北上,西北移与实际历史移行路径概括率达97%。
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     In new BP algorithm ,nonlinear function information of neural network output error is adopted, so it has faster partial convergence speed than that of general BP algorithm, but gradient descend method is used in FBP, which brings about local minimum problem.
     FBP算法吸收了误差函数的非线性信息,大大加快了BP算法的收敛速度,但它仍然采用梯度下降法,不可避免地存在局部极小的缺陷.
短句来源
     BP neural network is applied in this paper because of its nonlinear approximation function. The settling tank cost model is established by using it. The calculating surface area and volume are respectively used as network inputs,while the cost is used as the network output.
     运用BP网络的非线性函数逼近功能,以沉淀池的计算表面积与计算体积为网络的输入,沉淀池的费用值为网络的输出,建立单体构筑物沉淀池的费用模型.
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  相似匹配句对
     NETWORK
     网络技术
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     NETWORK
     网络
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     CRYPTANALYSIS ON MULTI-OUTPUT FEEDFORWARD NETWORK SYSTEM
     多输出前馈函数的一种相关分析方法
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     The Output Access of Control Signal in Adaline Network
     Adaline网络控制中控制信号的输出处理
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     Cost and output;
     成本与产出的关系;
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  network output
The feasible input vectors were propagated through the trained network and the network output was used to select the optimum yield estimate.
      
Our approach consists of two run-time techniques: (1) a statistical learning tool that detects unforeseen data; and (2) a reliability measure of the neural network output after it accommodates the environmental changes.
      
We study the long-run stability of the network output, establishing two-sided bounds for output perturbation via input perturbation.
      
The relative phase of activity between neurons in these networks is often a determinant of the network output.
      
Whereas both Laxon and Paxon had powerful and consistent effects on network output, the effects of λ on burst duration and rostro-caudal delay were more variable and depended on the values of the other two parameters.
      
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An approach for caculating sensitivity of electronic circuits is presented. The approach is based on the incremental network, and some improvements are obtained. The main advantage is that it can calculate sensitivities of all network outputs with respect to all network parameters by solving a system of linear equations only once. A simple example is given to illustrate its application.

本文给出了了一种求电路灵敏度的方法,即在增量网络法的基础上,对算法作了改进。该方法的优点是它只要求解线性方程组一次,就可以算出所有网络输出对所有网络参数的灵敏度阵。文中给出了一个简单例子。

In this paper, the property that a multi-layered neural network can describe any bounded nonlinear function is analyzed and verified by simulation experiment. The effect of the number of hidden layer neurons on network output is also analyzed, and the simulation results are given.

本文采用计算机仿真的方法,对多层神经网络描述任意有界非线性函数的特性进行了分析和实验验证,并对网络的隐层数和每一隐层的神经元个数给网络输出带来的影响进行了分析,同时给出了仿真结果。

In this paper, we propose an Equal-Error Range Approximation and Shrinking Learning Algorithm for multilayer perceptrons. It requires the error between each network output node activation and its target to fall into a given error range, thus it can learn faster in lower calculation cost and may avoid reversed target output and overlearning. hence itcan improve networks'generalization abilities in pattern recognitions. Through gradually Shrinking of the error range, it can also enable the networks...

In this paper, we propose an Equal-Error Range Approximation and Shrinking Learning Algorithm for multilayer perceptrons. It requires the error between each network output node activation and its target to fall into a given error range, thus it can learn faster in lower calculation cost and may avoid reversed target output and overlearning. hence itcan improve networks'generalization abilities in pattern recognitions. Through gradually Shrinking of the error range, it can also enable the networks to learn the targets more accurately in less training iterations. Finally, we apply this learning algorithm trained network to the EEG detection, and the experiment results have showed the above advantages of the proposed algorithm.

本文提出了一种用于前馈型多层神经网络学习的等误差范围逼近与收缩学习方法,这种方法仅仅要求网络的实际输出落在理想模式输出的一个事先给定的误差范围之内,从而可以大大提高网络的学习速度,且运算量小,而且通过适当选择等误差范围,它还可以提高网络在模式识别中的推广性能.如果网络用于模式联想等方面时,通过误差范围的逐步收缩,这种方法还可以以很小的额外代价提高网络学习的逼近精度;另外,它还可以避免传统方法中经常出现的训练模式反转等局域极小状态和过学习现象的出现.最后,文中给出了以这种方法训练的网络用于脑电波癫痫信号识别中的实验结果及其分析.

 
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