# Algorithm

## Algorithm

**1:**Initialize the:- network weights $\mathbf{u}_k=\left(u_{k 1}, u_{k 2}, \cdots, u_{K I}\right)$
- the learning rate $\eta$
- neighbourhood radius $\kappa$
*While stopping conditions are not met:*

**2:**for each input pattern $p$:- compute the Euc distance $d_{k, p}$ between $\mathbf{z}_p$ and $\mathbf{u}_k$
- Find the winning output unit $o_k$ (smallest $d_{k, p}$)
- Update weights for the neighbourhood $\kappa_{k, p}$

**3:**Update learning rate the learning rate $\eta(t)$**4:**Reduce neighbourhood radius $\kappa(t)$