Artificial Neural networks (ANN) consist of a fixed topology of artificial
neurons connected by links in a predefine manner. They can perform
information processing in a manner similar to a biological brain where the
neuronal network, like biological network, is inherently nonlinear, highly
parallel, robust and fault tolerant. The links represent synaptic weights and they
are adapted during learning. ANN can easily handle imprecise, fi.mzy,noisy and
probabilistic information and can generalize it to unknown tasks.
Due to powerful learning algorithms available and multi-layered structure a
neural network is flexible enough to handle any data-mining task for all practical
purposes. From this viewpoint, ANN can be seen as the most convenient sol3-
computing paradigm for data mining. The relationships present in the data set are
represented as multivariable fictional approximation where neurons play the
role of nonlinear models with global or local nonlinearity. With the global
nonlinearity, a neural network is widely regarded as a black box that it reveals
little about its predictions but it provides ease in applications to data-mining.
