Counterpropagation Neural Networks

Counterpropagation Neural Networks (CPN) were devloped by Professor Robert Hecht-Nielsen in 1987. CPN neural networks are a hybrid neural network, employing characteristics of both a feedforward neural network and a self-organizing map (SOM). The CPN is composed of three layers, the input, the instar and the outstar. The connection from the input to the instar layer is competitive, with only one neuron being allowed to win. The connection between the instar and outstar is feedforward. The layers are trained separately, using instar training and outstar training.

Counterpropagation Neural Networks (CPN) were devloped by Professor Robert Hecht-Nielsen in 1987. CPN neural networks are a hybrid neural network, employing characteristics of both a feedforward neural network and a self-organzing map (SOM). The CPN is composed of three layers, the input, the instar and the outstar. The connection from the input to the instar layer is competitive, with only one neuron being allowed to win. The connection between the instar and outstar is feedforward. The layers are trained separately, using instar training and outstar training.