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学习网络结构
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  learning structure
     Learning structure from incomplete data is one of the difficulties of learning Bayesian networks because of computational complexity.
     从不完备数据中学习网络结构是 Bayesian网学习的难点之一 ,计算复杂度高 ,实现困难 .
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  “学习网络结构”译为未确定词的双语例句
     For identification of nonlinear systems using recurrent high order neural networks, a set of available robust stable learning rules and the corresponding structures based on the Lyapunov stability method are presented. It ensures that the identification error and parameters of the neural networks are stable in the sense of uniform ultimate boundedness when a nonlinear system is identified, even in the case there exists modeling error.
     将高阶动态神经网络作为非线性系统的辨识模型 ,运用 Lyapunov稳定性理论 ,提出一种有效的鲁棒稳定学习规则及相应的学习网络结构 ,从而确保在对非线性系统辨识时 ,即使存在建模偏差 ,辨识误差和动态神经网络的参数能一致最终有界 (UUB)稳定 ,解决了动态神经网络的学习稳定性问题 .
短句来源
     The projection pursuit learning network to approximate multiple dimensional non-linear time series and the algorithm are presented in this paper, which can approximate the modes of multiple dimensional non-linear time series at any accuracy. The modeling and forecasting of multiple dimensional non-linear time series based on projection pursuit are solved, and a real example indicates that the algorithm is feasible.
     建立了多维非线性时间序列投影寻踪学习网络结构及算法,证明了投影寻踪学习网络可以以任意精度逼近多维非线性时间序列,解决了基于投影寻踪学习的多维非线性时间序列的建模和预测问题,实际应用例子表明该算法可行。
短句来源
     It adapts two-phase modus operandi i.e.s tructure learning and parameter learning. First,it learns the network structure through self-organization by samples; and then learns the network parameters i n concentrated batch.
     网络首先根据样本自组织学习网络结构,而后再进行集中批学习网络参数。
短句来源
     The network takes on two-phase models:first, studies the network structure by samples; and then studies the network parameters in concentrated batch.
     网络采用两阶段工作模式,首先根据中药材样本特征学习网络结构,而后再进行集中批学习网络参数。
短句来源
     In this paper, the Albus CMAC and its extensions are introduced ,a review of the research and application in CMAC and development trend are presented.
     小脑模型是一种局部学习网络,结构简单,收敛速度快,易于软硬件实现,因而具有广泛的应用前景,最后预测了小脑模型未来的发展趋势。
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     The structure of SOM network and learning algorithm is introduced.
     介绍了SOM网络结构学习算法;
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     Structure and training parameters of networks were optimized.
     进行了网络结构学习参数的优化;
短句来源
     The Construction of Network Firewall
     网络防火墙结构
短句来源
     Learning Bayesian Network Structure
     贝叶斯网络结构学习分析
短句来源
     Study on Learning Algorithms of Causality Diagram Structure
     因果图网络结构学习算法研究
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  learning structure
The success of this program is dependent on the integration of its courses within a logical learning structure; the close coordination of effort of teaching, tutoring, and administrative personnel; and the support of the college administration.
      
An artificial neural network (ANN) serves as the learning structure, and an applied stochastic real-valued (SRV) unit as the learning method.
      
Three categories of learning structure are examined: formal, nonformal and informal.
      
Learning Structure from Data and Its Application to Ozone Prediction
      
For OL to take place through KMS, our findings suggest that a deliberate organizational learning structure must be in place within the organization.
      
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Learning structure from incomplete data is one of the difficulties of learning Bayesian networks because of computational complexity. In this paper, an evolutionary algorithm combined with expectation is proposed. Fitness function is presented, which based on expectation, converts incomplete data to complete data utilizing current best structure of evolutionary process to reduce computational complexity, ensuring that this algorithm can evolve for good structure. Besides, encoding is given, and genetic operators...

Learning structure from incomplete data is one of the difficulties of learning Bayesian networks because of computational complexity. In this paper, an evolutionary algorithm combined with expectation is proposed. Fitness function is presented, which based on expectation, converts incomplete data to complete data utilizing current best structure of evolutionary process to reduce computational complexity, ensuring that this algorithm can evolve for good structure. Besides, encoding is given, and genetic operators are designed, which provides guarantee of convergence. Experimental results show that this algorithm can effectively learn Bayesian network structure from incomplete data.

从不完备数据中学习网络结构是 Bayesian网学习的难点之一 ,计算复杂度高 ,实现困难 .针对该问题提出了一种进化算法 .设计了结合数学期望的适应度函数 ,该函数利用进化过程中的最好 Bayesian网把不完备数据转换成完备数据 ,从而大大简化了学习的复杂度 ,并保证算法能够向好的结构不断进化 .此外 ,给出了网络结构的编码方案 ,设计了相应的遗传算子 ,使得该算法能够收敛到全局最优的 Bayesian网结构 .模拟实验结果表明 ,该算法能有效地从不完备数据中学习 .

For identification of nonlinear systems using recurrent high order neural networks, a set of available robust stable learning rules and the corresponding structures based on the Lyapunov stability method are presented. It ensures that the identification error and parameters of the neural networks are stable in the sense of uniform ultimate boundedness when a nonlinear system is identified, even in the case there exists modeling error. The simulation shows that the proposed learning rules are effective.

将高阶动态神经网络作为非线性系统的辨识模型 ,运用 Lyapunov稳定性理论 ,提出一种有效的鲁棒稳定学习规则及相应的学习网络结构 ,从而确保在对非线性系统辨识时 ,即使存在建模偏差 ,辨识误差和动态神经网络的参数能一致最终有界 (UUB)稳定 ,解决了动态神经网络的学习稳定性问题 .仿真结果也证明了该辨识方法的有效性

The projection pursuit learning network to approximate multiple dimensional non-linear time series and the algorithm are presented in this paper, which can approximate the modes of multiple dimensional non-linear time series at any accuracy. The modeling and forecasting of multiple dimensional non-linear time series based on projection pursuit are solved, and a real example indicates that the algorithm is feasible.

建立了多维非线性时间序列投影寻踪学习网络结构及算法,证明了投影寻踪学习网络可以以任意精度逼近多维非线性时间序列,解决了基于投影寻踪学习的多维非线性时间序列的建模和预测问题,实际应用例子表明该算法可行。

 
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