Probabilistic neural-network structure determination for pattern classification

Mao K.Z.; Tan K.-C.

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Probabilistic neural-network structure determination for pattern classification

机译：模式分类的概率神经网络结构确定

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Network structure determination is an important issue in pattern classification based on a probabilistic neural network. In this study, a supervised network structure determination algorithm is proposed. The proposed algorithm consists of two parts and runs in an iterative way. The first part identifies an appropriate smoothing parameter using a genetic algorithm, while the second part determines suitable pattern layer neurons using a forward regression orthogonal algorithm. The proposed algorithm is capable of offering a fairly small network structure with satisfactory classification accuracy.

机译：网络结构确定是基于概率神经网络的模式分类中的重要问题。本文提出了一种监督网络结构确定算法。所提出的算法由两部分组成，并且以迭代方式运行。第一部分使用遗传算法识别适当的平滑参数，而第二部分使用正向回归正交算法确定适当的模式层神经元。所提出的算法能够提供具有令人满意的分类精度的相当小的网络结构。

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《IEEE Transactions on Neural Networks》 |2000年第4期|P.1009-1016|共8页
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Mao K.Z.; Tan K.-C.;
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正文语种 eng
中图分类自动化技术、计算机技术;
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Probabilistic neural-network structure determination for pattern classification

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