Evolving from neuro-biological insights, neural network technology gives a computer system an amazing capacity to actually learn from input data. Artificial neural networks have provided solutions to problems normally requiring human observation and thought processes. Some real world applications include:

• Quality Control
• Financial Forecasting
• Economic Forecasting
• Credit Rating
• Speech & Pattern Recognition
• Biomedical Instrumentation
• Process Modeling & Management
• Laboratory Research
• Oil & Gas Exploration
• Health Care Cost Reduction
• Targeted Marketing
• Defense Contracting
• Bankruptcy Prediction
• Machine Diagnostics
• Securities Trading

At the core of neural computation are the concepts of distributed, adaptive, and nonlinear computing. Neural networks perform computation in a very different way than conventional computers, where a single central processing unit sequentially dictates every piece of the action. Neural networks are built from a large number of very simple processing elements that individually deal with pieces of a big problem. A processing element (PE) simply multiplies an input by a set of weights, and a nonlinearly transforms the result into an output value (table lookup). The principles of computation at the PE level are deceptively simple. The power of neural computation comes from the massive interconnection among the PEs which share the load of the overall processing task, and from the adaptive nature of the parameters (weights) that interconnect the PEs.

Normally, a neural network will have several layers of PEs. The most basic and commonly used neural network architecture is the multilayer perceptron (MLP). The diagram below illustrates a simple MLP. The circles are the PEs arranged in layers. The left row is the input layer, the middle row is the hidden layer, and the right row is the output layer. The lines represent weighted connections (i.e., a scaling factor) between PEs.

A Simple Multilayer Perceptron

The performance of an MLP is measured in terms of a desired signal and an error criterion. The output of the network is compared with a desired response to produce an error. An algorithm called backpropagation is used to adjust the weights a small amount at a time in a way that reduces the error. The network is trained by repeating this process many times. The goal of the training is to reach an optimal solution based on the performance measurement.

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