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网络最优结构
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  optimal network structure
Multiple query forwarding strategies are possible and, dependent on that strategy, an optimal network structure can be envisioned.
      
This will increase absorptive capacity, and so change the optimal network structure to be more random.
      
These developments are changing the optimal network structure and reducing costs.
      
The discussion on the optimal network structure of social capital can be summarized in the contingency model presented Figure 1.
      


This paper proposes a procedure for dynamically generating the optimal topologyof a multilayer feedforward neural network and demonstrates its effectiveness in functionapproximation. The algorithm applies an adaptive create-and-evaluate procedure whichevaluates the performance of the network and modifies it based on a series of criteria whoseparameters are dynamically adapted according to accumulated learning experiences. Thestructure of the network is modified by adding or deleting nodes or layers. Several...

This paper proposes a procedure for dynamically generating the optimal topologyof a multilayer feedforward neural network and demonstrates its effectiveness in functionapproximation. The algorithm applies an adaptive create-and-evaluate procedure whichevaluates the performance of the network and modifies it based on a series of criteria whoseparameters are dynamically adapted according to accumulated learning experiences. Thestructure of the network is modified by adding or deleting nodes or layers. Several functionapproximation cases are used to demonstrate the effectiveness of the procedure.The result ispromising.

本文提出了一种动态生成多层神经网络最优结构的方法。该方法利用基于经验规则的自适应方法对网络的学习过程进行评价,并根据其结果对网络的结构做出相应的改变。网络结构的修改是通过增加或删除隐含层结点及层数来实现的。该方法可根据问题的复杂度不同而产生与其相应的最优网络结构。我们用计算机仿真函数拟合实验来检验算法,取得满意的实验结果。

In this paper, a novel method for Kohonen network designing and training by using evolutional programming(EP) is proposed, which can autolnatically search the optimum structwe and connechon weights of the Kohonen network. This kind of Kohonene network is used for passive sonar targets clustering analysis, and the result of experiment shows that the EP based Kohonen network is effective in clustering the overlap signals of passive sonar target.

提出了一种用进化规划来设计和训练Kohonen网络的新方法,能够自动地确定网络的最优结构和联结权重.并将该网络用于被动声纳目标的聚类分析,实验结果表明基于进化规划的Kohonen网络能够有效地克服被动声呐目标信号的类间混叠现象.

This paper presents a

提出了一种新的基于专家系统的无分流换热器网络超结构模型,建立了换热器网络同步优化的数学模型,通过求解该模型能自动地获得换热网络的最优结构.通过示例说明了本文提出的超结构模型的有效性.

 
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