C#

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.

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The Radial Basis Function neural network contains a hidden layer based on radial basis functions (RBF). A radial basis function is a function that peaks in the center and rapidly falls off in each direction along an axis. One of the most common examples of a RBF is the Gaussian function. The hidden layer consists of one or more RBF's. This allows for a complex function to be modeled inside of the hidden layers. RBF neural networks are used for a variety of purposes, such as function approximation and prediction.

A neural network is a mathematical construct that attempts to emulate the way that actual neural networks, in the brain, work. A neural network accepts input and produces output. The input and output are usually both floating point arrays. Neural networks can be implemented in a variety of programming languages. This page focuses on using neural networks with C#.

    Hopfield networks can be much larger than four neurons. For the third example, we will examine a 64-neuron Hopfield network. This network is connected to an 8x8 grid, which an application allows you to draw upon. As you draw patterns, you can either train the network with them or present them for recognition.

    The user interface for the application can be seen in Figure 3.3.

Figure 3.3: A pattern recognition Hopfield application.

    Now you will see how to make use of the HopfieldNetwork class that was created in the last section. The first example implements a simple console application that demonstrates basic pattern recognition. The second example graphically displays the weight matrix using a C# application. Finally, the third example uses a C# application to illustrate how a Hopfield neural network can be used to recognize a grid pattern.

    The first example, which is a simple console application, is shown in Listing 3.2.

    The Hopfield neural network is implemented using two classes. The first class, called HopfieldNetwork is the main class that performs training and pattern recognition. This class relies on the Matrix and MatrixMath classes, introduced in chapter 2, to work with the neural network's weight matrix. The second class, called HopfieldException, is an exception that is raised when an error occurs while processing the Hopfield network. This is usually triggered as a result of bad input.

Introduction to Neural Networks with C#, Second Edition, introduces the C# programmer to the world of Neural Networks and Artificial Intelligence. Neural network architectures, such as the feedforward, Hopfield, and self-organizing map architectures are discussed. Training techniques, such as backpropagation, genetic algorithms and simulated annealing are also introduced. Practical examples are given for each neural network. Examples include the traveling salesman problem, handwriting recognition, financial prediction, game strategy, mathematical functions, and Internet bots.