Sergei Alderman's Artificial Neural Networks

Artificial neural networks (ANNs) have proven to be very valuable tools in such diverse applications as robotic control, fraud detection, and spam filtering. But-as valuable as ANNs are-they are also very complex, and often poorly understood.  They are generally treated as "black box" systems-input is fed in and output is tested for reliability without regard for how this transformation is accomplished. However, a deeper understanding of exactly how an ANN works can enhance its usefulness. This understanding is best gained with a historical, hands-on approach, by examining the progress of the methodology from the perceptron to the multilayer feedforward network (MFN) with backpropagation by designing an example network and putting it through its paces at each development level, as each step in that progress has added a new layer to the networks, both of greater generalization of usefulness and of greater complexity. This deeper understanding of the inner workings of ANNs will demonstrate why they have been so successful in certain types of applications, and what sorts of applications they would likely be useful for in the future.