A neural network is a powerful data modeling tool that is able to capture and represent complex input/output relationships.

Neural Networks are an information processing technique based on the way biological nervous systems, such as the brain, process information. They resemble the human brain in the following two ways:

1. A neural network acquires knowledge through learning.
2. A neural network's knowledge is stored within inter-neuron connection strengths known as synaptic weights.

Neural networks are being applied to an increasing large number of real world problems. Their primary advantage is that they can solve problems that are too complex for conventional technologies; problems that do not have an algorithmic solution or for which an algorithmic solution is too complex to be defined. In general, neural networks are well suited to problems that people are good at solving, but for which computers generally are not. These problems include pattern recognition and forecasting, which requires the recognition of trends in data.

The true power and advantage of neural networks lies in their ability to represent both linear and non-linear relationships and in their ability to learn these relationships directly from the data being modeled. Traditional linear models are simply inadequate when it comes to modeling data that contains non-linear characteristics.

The MLP and many other neural networks learn using an algorithm called backpropagation. With backpropagation, the input data is repeatedly presented to the neural network. With each presentation the output of the neural network is compared to the desired output and an error is computed. This error is then fed back (backpropagated) to the neural network and used to adjust the weights such that the error decreases with each iteration and the neural model gets closer and closer to producing the desired output. This process is known as "training".

Neural networks have been successfully applied to broad spectrum of data-intensive applications, such as:

• Process Modeling and Control - Creating a neural network model for a physical plant then using that model to determine the best control settings for the plant.
• Machine Diagnostics - Detect when a machine has failed so that the system can automatically shut down the machine when this occurs.
• Portfolio Management - Allocate the assets in a portfolio in a way that maximizes return and minimizes risk.
• Target Recognition - Military application which uses video and/or infrared image data to determine if an enemy target is present.
• Medical Diagnosis - Assisting doctors with their diagnosis by analyzing the reported symptoms and/or image data such as MRIs or X-rays.
• Credit Rating - Automatically assigning a company's or individuals credit rating based on their financial condition.
• Targeted Marketing - Finding the set of demographics which have the highest response rate for a particular marketing campaign.
• Voice Recognition - Transcribing spoken words into ASCII text.
• Financial Forecasting - Using the historical data of a security to predict the future movement of that security.
• Quality Control - Attaching a camera or sensor to the end of a production process to automatically inspect for defects.
• Intelligent Searching - An internet search engine that provides the most relevant content and banner ads based on the users' past behavior.
• Fraud Detection - Detect fraudulent credit card transactions and automatically decline the charge.