Dynamic Fuzzy Machine Learning

At the same time, $\exists n \leq | (\overset{\leftarrow}{V_{\overset{\leftarrow}{A_{1}}}}), \vec{V_{\vec{A_{1}}}} |$ such that $p a r_{(\overset{\leftarrow}{A_{a}} . \vec{A_{a}})}^{(\overset{\leftarrow}{v_{j}} . \vec{v_{j})}} (\overset{\leftarrow}{U,} \vec{U}) \subseteq \cup_{k = 1}^{N}$ $p a r_{(\overset{\leftarrow}{A_{i}} . \vec{A_{i}})}^{(\overset{\leftarrow}{v_{k}} . \vec{v_{k})}} (\overset{\leftarrow}{U,} \vec{U})$ holds, and the membership rate of sample class $({\overset{\leftarrow}{v}}_{j}, {\vec{v}}_{j})$ divided by condition attribute $(\overset{\leftarrow}{A_{i}}, \vec{A_{i}})$ is written as

$C e r_{({\overset{\leftarrow}{A}}_{1}, \vec{A_{t}})}^{i} (\overset{\leftarrow}{U}, \vec{U}) = \max (| P a r \overset{\leftarrow}{\frac{(v_{k}}{(A_{t}}} \vec{\frac{v_{k}}{A_{t}}} (\overset{\leftarrow}{U}, \vec{U}) |) / \min (| \cup_{k = 1}^{π} P a r \overset{\leftarrow}{\frac{(v_{k}}{(A_{l}}} \vec{\frac{v_{k}}{A_{l}}} (\overset{\leftarrow}{U}, \vec{U}) |),$

where $P a r_{(\overset{\leftarrow}{A_{i}} . \vec{A_{i}})}^{(\overset{\leftarrow}{v_{k}} . \vec{v_{k})}} (\overset{\leftarrow}{U,} \vec{U}$

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Dynamic Fuzzy Machine Learning by Fanzhang Li, Li Zhang, Zhao Zhang

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