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This article shows how the Multi Propagation (MPROP) algorithm was implemented for Encog for Java. Though this article focuses on the Java implementation the C# version would be very similar. MPROP is based on resilient propagation, but is designed to work well with multicore computers and gain maximum performance.

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.

The ADALINE neural network was developed by professor Bernard Widrow and his graduate student Ted Hoff. It is a very simple neural network usually used for pattern recognition. ADALINE is short for Adaptive Linear Neuron or Adaptive Linear Element. It is considered a single layer neural network. Though an activation function forms a sort of primitive output layer. Weighted connections are made to this activation function. A threshold, or bias, is also provided for. The output from an ADALINE neural network is usually bipolar.

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.

The ADALINE neural network was developed by professor Bernard Widrow and his graduate student Ted Hoff. It is a very simple neural network usually used for pattern recognition. ADALINE is short for Adaptive Linear Neuron or Adaptive Linear Element. It is considered a single layer neural network. Though an activation function forms a sort of primitive output layer. Weighted connections are made to this activation function. A threshold, or bias, is also provided for. The output from an ADALINE neural network is usually bipolar.