The research on uncertainty is a hot spot in artificial intelligence, especially in actual applications. Most research focus on measuring the uncertainty between evidence, ignoring the structure of evidence. When a sample has multiple features, the traditional structure of the focal element cannot handle and display the uncertainty between multiple features directly. Hence, we proposed a new structure of the focal element considering multiple features into one focal element and provided the corresponding basic probability assignment calculation. To apply the new structure in practices, we provided a new object recognition method combining the fractal feature and credible evidence. The new method can improve the robustness of the algorithm in a noisy environment. In the end, some experiments have illustrated the validity and correctness of the proposed method.

中文翻译：

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物体识别中焦点元素的新结构

不确定性研究是人工智能领域的热点，尤其是在实际应用中。大多数研究侧重于衡量证据之间的不确定性，而忽略了证据的结构。当一个样本具有多个特征时，传统的焦元结构无法直接处理和显示多个特征之间的不确定性。因此，我们提出了一种新的焦元结构，将多个特征合并为一个焦元，并提供了相应的基本概率分配计算。为了在实践中应用新结构，我们提供了一种结合分形特征和可信证据的新对象识别方法。新方法可以提高算法在噪声环境中的鲁棒性。到底，