Dynamic Fuzzy Machine Learning

Theorem 1.2 If each point between the noise and between different dimensions, and between ${\left({\overleftarrow{W}}_i,{\overleftarrow{W}}_i\right)}^{\text{'}}$ and the noise are independent of each other, the noise has a mean value with the same variance [14]. Then, $\delta \left(\overleftarrow{W},\overrightarrow{W}\right)=\left({\overleftarrow{W}}_i,{\overrightarrow{W}}_i\right)\text{'}-\left({\overleftarrow{W}}_i,{\overrightarrow{W}}_i\right)$ can be estimated as

where ‖⋅‖ is the Euclidean norm; ${\left(\overleftarrow{\delta },\overrightarrow{\delta }\right)}^2={\displaystyle \sum _{j=1}^D{\left({\overleftarrow{\delta }}_i,{\overleftarrow{\delta }}_i\right)}^2,\left({\overleftarrow{\delta }}_i,{\overleftarrow{\delta }}_i\right)=Var\left({\overleftarrow{\epsilon }}_i,{\overleftarrow{\epsilon }}_i\right)}$$\left(i=1,2,\mathrm{...},D\right),\left({\overleftarrow{\epsilon }}_i,{\overleftarrow{\epsilon }}_i\right)$ represents ...