The CounterPropagation algorithm updates a neural network with an input, hidden, and output layer. It identifies the hidden neuron with the highest input, setting its activation to 1 and others to 0. The output is then calculated as the weighted sum of the hidden neuron, equal to the weight of the link between the winner hidden neuron and the output neurons. This update works with the CounterPropagation learning function to train the network.

2. CounterPropagation The CounterPropagation update algorithm updates a net that consists of a input, hidden and output layer. In this case the hidden layer is called the Kohonen layer and the output layer is called the Grossberg layer. At the beginning of the algorithm the output of the input neurons is equal to the input vector. The input vector is normalized to the length of one. Now the progression of the Kohonen layer starts.

3. This means that a neuron with the highest net input is identified. The activation of this winner neuron is set to 1. The activation of all other neurons in this layer is set to 0. Now the output of all output neurons is calculated. There is only one neuron of the hidden layer with the activation and the output set to 1.

4. This and the fact that the activation and the output of all output neurons is the weighted sum on the output of the hidden neurons implies that the output of the output neurons is the weight of the link between the winner neuron and the output neurons. This update function makes sense only in combination with the CPN learning function.

13. Dudas ???

14. Hasta la próxima !!!