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where represents the input pattern, is the total number of SOM neurons, and the input pattern has dimensions.
Hence, for a given input pattern, only neurons whose centers are close to the input pattern will produce non-zero activation values to the input stimulus.
They simply take the corresponding value from the input pattern vector.
The output class label, corresponding to the th input pattern, is determined as (14).
The GRNN structure consists of four layers: input, pattern, summation and output layer (Fig. 1).
Values shown are averages over 40 runs with different initial weights and a different input pattern.
The principle used to perform the decision of classification on an input pattern is explained.
Each input pattern contains a fixed number of input elements or input processing elements (PEs).
When learning ends, the cell's response favours one input pattern over others to exhibit feature selectivity.
Moreover, nonlinearity of each module is introduced by enhancing the input pattern with nonlinear functional expansion.
In the forward pass, an input pattern vector is presented to the network and the output of the input layer nodes is precisely the components of the input pattern.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com