Source: www.learnartificialneuralnetworks.com

Artificial neural networks (ANNS) are biologically inspired. Specifically, they borrow ideas from the way the human brain works. The human brain is composed of special cells called neurons. Estimates of the number of neurons in human brain cover a wide range (up to $ 150 billion), and there are more than 100 different types of neurons, separated into groups called networks. Each network contains several thousand neurons that are highly interconnected. Thus, the brain can be considered as a collection of neural networks.

ANNs of today, which the application is called neural computing, use a limited set of concepts of biological neural systems. The objective is to simulate massive parallel processes that involve processing elements interconnected in a network architecture. The neuron receives similar inputs for electrochemical pulses of biological neurons receive other neurons. The output of the artificial neuron is sent signals of a biological neuron. These artificial signals can be changed, as the signals of the human brain. Neurons in an ANN receive information from other neurons or external source, transform or process the information and pass on other neurons or outputs external.

The manner in which an ANN treats information depends on its structure and algorithm used to process information.

Benefits and Applications of neural networks

Neural network technology value includes its usefulness for pattern recognition, learning and interpretation of input incomplete and "noisy".

Neural networks are likely to provide some of the human characteristics of resolution of problems that are difficult to simulate using logical techniques, analyses of MAS or even expert systems. One of these features is pattern recognition. Neural networks can analyze the large amounts of data to establish the characteristics and trends in situations where logic and rules are not known. An example would be loan applications. In reviewing the many historical cases of questionnaires for the applicants and the "Yes or no" decisions, the ANN can create "patterns" or "profiles" of applications should be approved or denied. A new application can then connect the computer against the pattern. If what is close enough, the computer it classifies as a "Yes" or "no"; otherwise, it goes to a man for a decision. Neural networks are particularly useful for financial applications such as the determination of the case of buy or sell the stock, predicting bankruptcy and predicting exchange rates.

His role as another mechanism in data mining, neural computing and calculation can be combined with other systems of information on the computer to produce powerful hybrid systems.

Neural computing is emerging as an effective technology in recognition of forms. This capability translates into many applications and is sometimes integrated into fuzzy logic.