The initial weights and biases of the artificial neural network(ANN)are obtained by offline training method.

人工神经网络（ANN）的初始权值和阈值通过离线训练的方式获得。

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The extracted initial rules and their accuracy and coverage are used to configure the fuzzy perceptron structure and initial weights for training.

网络的结构由已经抽取的规则映射而成，初始连接权由规则的精确度和覆盖度确定。

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With this model, the initial weights and threshold values of the neural network are optimized using GA to avoid the possibility of local search minimum.

该模型采用GA对神经网络的初始权值和阈值进行优化，以避免可能的局部搜索最小现象。

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Compared with general ANN design, this paper puts forward a new operator, BP operator, and optimizes the ANN's initial weights and structure at the same time.

本文与一般的基于遗传算法的神经网络设计相比，提出一个新型算子——BP算子，并对神经网络的权值和结构同时优化。

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Since an FCM based self adaptive fuzzy clustering technique is employed to determine the proper structure of the FNN and set the initial weights in advance, the network can be trained rapidly.

由于预先运用基于FCM的自适应模糊聚类方法确定模糊神经元网络合理的结构，并设置网络的初始权值，从而可提高网络的训练速度。

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In initial placement we propose a new way giving weights to the nets, and in iterative placement, we present a concept called Equal Position Field.

在初始布局中，我们提出了给线网加权的新方法，在迭代改善布局中提出了等位场的概念。

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In initial placement we propose a new way giving weights to the nets, and in iterative placement, we present a concept called Equal Position Field.

在初始布局中，我们提出了给线网加权的新方法，在迭代改善布局中提出了等位场的概念。

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