In general neural networks are simply mathematical techniques designed to accomplish a variety of tasks. Neural networks can be configured in various arrangements to perform a range of tasks including pattern recognition, data mining, classification, and process modeling. The later is the primary interest in this context and although the types and topologies of neural networks (NN) vary greatly in the field, by far the most commonly used type of neural network, particularly in process control, is the feed-forward, back-propagation NN.

The theory and even implementation of neural networks has been around for better than 50 years; however, only recently have NNs found wide spread, practical application. This is due primarily to the advent of high speed, low cost computers that can support the rather computationally intensive requirements of a NN of any real complexity.

The conceptual constructs of a NN stemmed from our early understanding of the human brain. As you probably know, the brain is comprised of billions and billions of interconnected neurons (some experts estimate upwards of 1011 neurons in the human brain). The fundamental building blocks of this massively parallel cellular structure are really quite simply when studied in isolation. A neuron receives incoming electrochemical signals from its dendrites and collects these signals at the neuron nucleus. The neuron nucleus has a internal threshold that determines if the neuron itself fires in response to the incoming information. If the combined incoming signals exceed this threshold then the neuron fires and an electrochemical signal is sent to all neurons connected to the firing neuron on its output connections or axons. Otherwise the incoming signals are ignored and the neuron remains dormant.

More soon!