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    赵温波, 王立明, 黄德双. 最大绝对误差结合微遗传算法优化径向基概率神经网络[J]. 计算机研究与发展, 2005, 42(2): 179-187.
    引用本文: 赵温波, 王立明, 黄德双. 最大绝对误差结合微遗传算法优化径向基概率神经网络[J]. 计算机研究与发展, 2005, 42(2): 179-187.
    Zhao Wenbo, Wang Liming, Huang Deshuang. Structure Optimization of Radial Basis Probabilistic Neural Networks by the Maximum Absolute Error Combined with the Micro-Genetic Algorithm[J]. Journal of Computer Research and Development, 2005, 42(2): 179-187.
    Citation: Zhao Wenbo, Wang Liming, Huang Deshuang. Structure Optimization of Radial Basis Probabilistic Neural Networks by the Maximum Absolute Error Combined with the Micro-Genetic Algorithm[J]. Journal of Computer Research and Development, 2005, 42(2): 179-187.

    最大绝对误差结合微遗传算法优化径向基概率神经网络

    Structure Optimization of Radial Basis Probabilistic Neural Networks by the Maximum Absolute Error Combined with the Micro-Genetic Algorithm

    • 摘要: 使用最大绝对误差算法(MAEA)优选径向基概率神经网络(RBPNN)隐中心矢量,将MAEA与求解RBPNN最优核函数控制参数的微遗传算法(μGA)相结合(MAE-μGA)来共同实现RBPNN的全结构优化.实验结果显示,对比其他几种算法,MAE-μGA优化后的RBPNN结构最简,而且在推广能力方面略好于其他几种优化方法.另外,MAE-μGA对径向基函数网络也有很好的适用性.

       

      Abstract: The maximum absolute error algorithm (MAEA) is used to optimally selecting the hidden centers vectors of the radial basis probabilistic neural networks (RBPNN). The MAEA is combined with the micro-genetic algorithm (μGA), which is used to optimize the controlling parameter of the kernel function of the RBPNN, i.e., MAE-μGA, so as to carry out optimizing the overall structure of RBPNN. The experiments demonstrate that the RBPNN, optimized by the MAE-μGA, has the best simple structure compared with results by the other optimization methods introduced. Furthermore, in the aspect of the generalization performance of the optimized networks, the RBPNN by the MAE-μGA is a little better than ones by the other methods. In addition, the MAE-μGA can also be used to optimize the radial basis function neural networks (RBFNN).

       

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