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.

Are you a Java or C# programmer? Would you like to contribute some time to the Encog project? We are always looking for volunteers, and at all skill levels. You do need to be proficient with Java or C#, but you by no means need to be an AI expert. We are, of course, glad to have AI experts help! But we usually have tasks available that do not require advanced knowledge of AI.

Suggesting New Features

    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.