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Basic Market Forecasting with Encog Neural Networks (Download Code)

    You are now familiar with all of the layer and synapse types supported by Encog. You will now be given a brief introduction to building ANNs with these neural network types. You will see how to construct several neural network types. They will be used to solve problems related to the XOR operator. For now, the XOR operator is a good enough introduction to several neural network architectures. We will see more interesting examples, as the book progresses. We will begin with the feedforward neural network.

    We will now look at how to structure a neural network for a very simple problem. We will consider creating a neural network that can function as an XOR operator. Learning the XOR operator is a frequent “first example” when demonstrating the architecture of a new neural network. Just as most new programming languages are first demonstrated with a program that simply displays “Hello World”, neural networks are frequently demonstrated with the XOR operator.

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.