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Hayato Nakada Author Vitae Kiyotsugu Takaba Author Vitae Author Vitae 《Automatica》2005,41(5):905-913
This paper is concerned with the identification of a class of piecewise affine systems called a piecewise affine autoregressive exogenous (PWARX) model. The PWARX model is composed of ARX sub-models each of which corresponds to a polyhedral region of the regression space. Under the temporary assumption that the number of sub-models is known a priori, the input-output data are collected into several clusters by using a statistical clustering algorithm. We utilize support vector classifiers to estimate the boundary hyperplane between two adjacent regions in the regression space. In each cluster, the parameter vector of the sub-model is obtained by the least squares method. It turns out that the present statistical clustering approach enables us to estimate the number of sub-models based on the information criteria such as CAIC and MDL. The estimate of the number of sub-models is performed by applying the identification procedure several times to the same data set, after having fixed the number of sub-models to different values. Finally, we verify the applicability of the present identification method through a numerical example of a Hammerstein model. 相似文献
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针对含过程调用EFSM模型测试数据生成中过程调用的不可执行问题,提出了一种能对过程调用进行处理以实现模型的测试数据自动生成方法。该方法将被调子过程描述为一个EFSM模型,将表示主过程及子过程的EFSM模型合并为一个新的模型,合并后的模型符合EFSM模型规范,采用遗传算法对该模型进行测试数据自动生成。实验结果表明,该方法能够较好地解决含过程调用EFSM模型的测试数据自动生成问题。 相似文献
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目的 针对传统Mean Shift算法对受背景干扰的目标无法进行有效跟踪并缺少有效的模型更新策略的问题,提出一种将背景加权和选择性子模型更新相结合的跟踪算法。方法 首先,在Mean Shift框架下,为了减少背景信息对目标定位的干扰,利用目标区域周围像素的颜色直方图定义背景加权系数,并将该系数只引入到目标模型的颜色直方图中,从而建立一个新的目标模型。然后,根据目标模型中每个分量匹配贡献度的大小选取需要更新的模型分量及其更新公式。结果 实验结果表明,本文算法能够抑制背景干扰,同时能对模型进行有效的选择性更新,克服了整体更新策略严重的模型漂移问题。结论 本文从模型描述和更新策略两个方面对传统Mean Shift算法进行了改进,实验结果表明本文算法具有较好的有效性和鲁棒性。 相似文献
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