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For many decades, it has been a goal of science and engineering to develop intelligent machines with a large number of simple elements. References to this subject can be found in the scientific literature of the 19th century. During the 1940s, researchers desiring to duplicate the function of the human brain, have developed simple hardware (and later software) models of biological neurons and their interaction systems. The first systematic study of the artificial neural network was published. A few years later, network paradigms for pattern recognition using a single layer perceptron were explored. In the 1950s and 1960s, a group of researchers combined these biological and psychological insights to produce the first artificial neural network (ANN). Initially implemented as electronic circuits, they were later converted into a more flexible medium of computer simulation. However, later these works were challenged
For a variety of reasons, the symbolic–processing approach became the dominant method. Moreover, the perceptron turned out to be more limited than first expected. Although further investigations in ANN continued during the 1970s by several pioneer researchers, their works received relatively less attention.
The primary factors for the recent resurgence of interest in the area of neural networks are the extension of Rosenblatt, Widrow and Hoff’s works dealing with learning in a complex, multi-layer network, Hopfield mathematical foundation for understanding the dynamics of an important class of networks, as well as much faster computers than those of 50s and 60s
The interest in neural networks is said to come from the networks’ ability to mimic human brain as well as its ability to learn and respond. As a result, neural networks have been used in a large number of applications and have proven to be effective in performing complex functions in a variety of fields. These include pattern recognition, classification, vision, control systems, and prediction . Adaptation or learning is a major focus of neural net research that provides a degree of robustness to the NN model.