2D SONN algorithm with decremental gain

Here’s a simple algorithm to produce Self-organizing neural networks (SONN) in 2D clustering problems with a simple decremented gain.

Number of clusters of input data x represented by N_n\times N_m, and N_F is the number of features in each cluster. To represent the amount of change in the weights as a function of the distance from the center cluster (n_0,m_0), here I use window function \lambda, and the goal is to decrement the gain g(t+1) for updating the weights at next step iteration.

Step 1 : set the weight in all clusters to random values :


for n=0,1,2,...,N_n; m=0,1,2,...,N_m; and i=0,1,2,...,N_F

Set the initial gain g(0)=1.

Step 2 : For each input pattern

x^t, where t = 1, 2, ...., k,

(a). Identify the cluster that is closest to to the k-th input  :

(n_0, m_0)=\displaystyle min_{j,l} {||x^k-w^{j,l}||}.

(b). Update the weights of the clusters in the neighborhood of cluster (n,m), N, according to the rule :

w_i^{n,m}(t+1) \leftarrow w_i^{n,m}(t)+g(t)\lambda (n,m)[x_i^k-w_i^{n,m}(t)], for (n,m) \in N,

Where \lambda(n,m) is window function.

Step 3 : Decrement the gain term used for adapting the weight :

g(t+1)=\mu g(t),

where \mu is the learning rate.

Step 4 : Repeat by going back to step 2 until convergence.

For the similar 1D SONN algorithm see here.

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