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An ANN can be defined as a mathematical and computational tool for nonlinear statistical data modeling, influenced by the structure and function of biological nervous system. A large number of immensely interconnected processing units, termed as neurons, build ANN.

Generally, ANN receives a set of inputs and produces the weighted sum, and then, the result is passed to the nonlinear function which generates the output. Like human being, ANN also learns by example. The models of ANN are required to be appropriately trained to generate the output efficiently. In biological nervous system, learning involves adaptations in the synaptic connections between the neurons. This idea influences the learning procedure of ANN. The system parameter of ANNs can be adjusted according to I/O pattern. Through learning process, ANN can be applied in the domains of data classification, pattern recognition, etc.

The researchers are working on ANN for past several decades. This domain has been established even before the advent of computers. The artificial neuron [1] was first introduced by Warren McCulloch, the neurophysiologist, and Walter Pits, the logician, in 1943.