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    基于奖惩学习算法的序参量重构方法

    RECONSTRUCTION OF ORDER PARAMETERS BASED ON AWARD\|PENALTY LEARNING MECHANISM

    • 摘要: 分析了协同方法中序参量在模式识别过程中存在的不合理因素 ,阐述了经过序参量重构的协同方法能够有效地克服这些不合理因素 ,从而提高模式识别性能 .为了获得序参量重构参数 ,提出了基于奖惩学习算法的重构参数的搜索算法 ,该算法结合协同神经网络的自学习能力和奖惩学习算法的搜索能力来训练序参量重构参数 .利用从实际应用中得到的样本对新算法进行的测试表明 ,新算法确实能找到一组序参量重构参数使识别性能得到较大提高 ,具有很好的实用性 .另外 ,还讨论了奖惩学习算法中参数 δ对新算法的训练性能的影响 ,以期指导参数 δ的选取 ,从而达到最佳的训练效果

       

      Abstract: An analysis of unreasonable factor in construction of order parameters in synergetic approach is presented in this paper. It is proved that the unreasonable factor during the dynamic system can be overcome through reconstruction of order parameters. A way of reconstruction of order parameters based on award\|penalty learning mechanism is proposed, which can figure out a group of linear transformation parameters for order parameters using self\|learning power of synergetic neural networks and award\|penalty learning mechanism. The test on samples from real application shows that the new approach can improve the recognition rate greatly and has bright application prospect. Additionally, in order to guide the selection of parameter δ to obtain the best train performance, the influence of parameter δ of award\|penalty learning mechanism on the performance of training is discussed.

       

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