-Insensitive Criterion and Structure Risk Based Radius-basis-function Neural-network Modeling
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摘要: 该文提出一种新的基于-不敏感准则和结构风险的径向基神经网络(RBF-NN)建模方法。通过引入-不敏感准则和结构风险项,把RBF-NN训练转化为线性回归和经典的二次规划优化问题。和传统的基于最小平方误差准则的RBF-NN训练算法相比,提出的新方法对小样本数据集和噪声数据显示出较好的鲁棒性,具有较好的泛化能力。通过模拟和真实数据集进行仿真试验,上述优点得到了有效验证。
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关键词:
- 径向基函数神经网络建模 /
- -不敏感准则 /
- 结构风险 /
- 鲁棒性
Abstract: An - insensitive criterion and structure risk based Radius-Basis-Function Neural-Network (RBF-NN) modeling method is proposed. By -introducing insensitive criterion and the item of structure risk, the RBF-NN learning is transformed into the linear regression and Quadratic Program (QP) optimization issue. Compared with the traditional least-square-criterion based RBF-NN training algorithms, the proposed method is much more robust to noise data and small size of datasets. Through the simulation experiments on the synthetic and real-word datasets, the above virtues are confirmed.
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