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感知器神经网络
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  perceptron neural network
     Application of multi layer perceptron neural network in mechanical fault diagnosis
     多层感知器神经网络在机械故障诊断中的应用
短句来源
     Design of waveguide matched load based on multilayer perceptron neural network
     基于多层感知器神经网络的波导匹配负载设计
短句来源
     The computer simulation results show that this kind of filter can reject narrowband interference efficiently and it is superior to other interference filter based on transversal filter or perceptron neural network.
     计算机仿真结果表明 ,当干扰为窄带信号时 ,利用径向基函数网络可以有效地对干扰进行抑制 . 将该方法与基于传统横向滤波器和感知器神经网络的干扰抑制器进行比较 ,结果表明该方法具有一定的优越性
短句来源
     Design of rectangular waveguide terminal matched load based on multilayer perceptron neural network (MLPNN) is presented.
     讨论了多层感知器神经网络 (MLPNN)在矩形波导终端匹配短负载设计中的应用。
短句来源
     7. Based on analyzing the multilayer perceptron neural network,it induces the error backpropagation algorithm while taking the hyperboloid tangential function as nonlinear activation function.
     7.在分析多层感知器神经网络的基础上,推导了双曲正切函数为激励函数的误差后向传播算法。
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  perceptron artificial neural network
     last distill the eigenvalues of the default, input them to the single floor (no concealed floor) perceptron artificial neural network to classify the defaults.
     最后提取疵点的特征值,输入单层(无隐层)感知器神经网络进行疵点的识别与分类。
短句来源
  apperceive neural network
     At last,the parameters are recognized and classified by apperceive neural network.
     最后 ,采用感知器神经网络对模型参数进行识别与分类。
短句来源
  “感知器神经网络”译为未确定词的双语例句
     ③selecting neural network with multi-layer sensor.
     3选用多层感知器神经网络
短句来源
     Classification for RS Fused Image and TM Image Using Multi - Layer Perception Neural Network
     基于多层感知器神经网络对遥感融合图像和TM影像进行土地覆盖分类的研究
短句来源
     Least Squares Support Vector Machines(LS-SVM),as a new machine learning method,has such properties as global convergence and good ability of extension. The research applies LS-SVM to data classification and prediction in terms of data mining. By kernel function selecting and parameter optimizing,the performance has been evaluated and compared with SVM,MLP neural network and discriminant analysis.
     最小二乘支持向量机作为一种新的机器学习方法,具有全局收敛性和良好的泛化能力,本文将其应用于数据挖掘的分类与预测研究,通过核函数的选择及参数优化,并结合支持向量机、多层感知器神经网络模型及判别分析方法进行比较研究,证明最小二乘支持向量机作为一种有效的数据挖掘算法具有较高精度。
短句来源
     At last ,we assess the health hierarchical of nine different forestry community by using all index of ecosystem'health ,at the same time ,we use the neural network technology in assessing the ecosystem health ,and make the assessment scientifically and objectively.
     4.最后,我们对各群落的所有有关健康指数进行综合分析,同时利用感知器神经网络技术对生态系统健康状况进行科学的等级分类,并和健康状况变化后的生态系统进行比较,从而对生态系统健康评价的进行现状分析和动态性比较分析。
短句来源
     The possibility of decoding convolutional codes by means of the multilayer perception neural entwork (NN) trained with the error back propagation (EBP) algorithm is considered.
     分析运用误差反向传播 (EBP)多层感知器神经网络 (NN)方法来进行卷积码译码的可能性 .
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  perceptron neural network
In order to solve the inverse problem, a perceptron neural network is constructed.
      
Forecasting automobile warranty performance in presence of 'maturing data' phenomena using multilayer perceptron neural network
      
Ethyl alcohol production optimization by coupling genetic algorithm and multilayer perceptron neural network
      
In this present article, genetic algorithms and multilayer perceptron neural network (MLPNN) have been integrated in order to reduce the complexity of an optimization problem.
      
The model has been generalized and implemented by means of a Multilayer Perceptron Neural Network that has been trained to simulate the system experimental dynamics.
      
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A new method for on-line intelligent control of workpiece dimension accuracyvia fuzzy multilayer perception networks is proposed. With good forecast in time,it is aneffective means on the occasions of lack of precise mathematical description or of timed-elayed and non-linear system. The principle on multilayer perception networks is given,and the three layer perception model and its learning algorithm derived from human kno-wledge on iatelligent control of workpiece dimension accuracy are described. With expe-rimental...

A new method for on-line intelligent control of workpiece dimension accuracyvia fuzzy multilayer perception networks is proposed. With good forecast in time,it is aneffective means on the occasions of lack of precise mathematical description or of timed-elayed and non-linear system. The principle on multilayer perception networks is given,and the three layer perception model and its learning algorithm derived from human kno-wledge on iatelligent control of workpiece dimension accuracy are described. With expe-rimental simulation, the method holds a promise of application.

提出了基于模糊多层感知器神经网络模型的尺寸加工精度在线智能监控新方法,它对于缺乏精确的数学描述或具有时滞、非线性等复杂实际对象,是一种行之有效的技术,并具有及时性和预报性好的特点。描述了多层感知器神经网络的工作原理,阐明了基于人的模糊经验规则实现工件尺寸精度智能监控的三层感知器控制模型和学习算法,并进行了试验仿真。结果表明,方法有效,具有应用前景。

A method of implementing symbol logic inference system using recurrent multilayer perceptron neural networks is presented in this paper.Domain rule knowledge can be either acquired through learning domain sample set by neural networks or encoded into neural networks directly.Once domain rule knowledge has been stored in a neural networks,the neural networks can be used to implement any symbol logic inference in that domain.It is a theoretical base for studying relations between the abstract thought of human(logic...

A method of implementing symbol logic inference system using recurrent multilayer perceptron neural networks is presented in this paper.Domain rule knowledge can be either acquired through learning domain sample set by neural networks or encoded into neural networks directly.Once domain rule knowledge has been stored in a neural networks,the neural networks can be used to implement any symbol logic inference in that domain.It is a theoretical base for studying relations between the abstract thought of human(logic symbol processing) and thinking in images of neural network(linked data calculating).

介绍一种用循环多层感知器神经网络实现符号逻辑推理系统的方法。该方法通过让神经网络学习训练样本获取领域规则知识,或者直接将领域规则知识编码于神经网络之中,即用神经网络来表达领域规则知识,然后通过神经网络的循环反馈计算过程来实现任意形式的符号逻辑推理.为研究人类抽象思维(逻辑符号推理)与神经网络形象思维(联接数值计算)之间的关系提供了理论基础。

In this paper,the authors study the detection of signals in non-Gaussian noise,and employ a multilayer perceptron neural network as a detector. The authors introduce the operating principle, network structure and training algorithm. By computer simulation,the authors demonstrate that in non-Gaussian noise,neural detectors outperform linear matched filter detectors and locally optimum detectors.

本文研究了非高斯噪声中信号的检测,采用多层感知器神经网络作为检测器。介绍了工作原理,网络结构和训练算法。计算机仿真证明在非高斯噪声条件下神经网络检测器性能优于线性最佳匹配滤波器检测器和局部最佳检测器。

 
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