ABSTRACT
This chapter describes the basics of ANN and discusses its applicability in various fields. Artificial neural network (ANN) is a biologically inspired learning algorithm designed to process information in the same way as the human brain. ANN is a versatile and powerful mathematical tool that can handle many complex tasks such as classification problems, time series, and function approximation. The work in the field of ANN started in the late twentieth century, when in 1943 McCulloch and Pitts introduced the first computational model of a neuron inspired by the human brain. The 1970s saw the progression in research on artificial neural networks in various fields such as pattern recognition, biological modeling, and signal processing, to name a few. The structure of a neuron comprises four main parts: soma or the cell body, a synaptic terminal, and two offshoots from soma called dendrites and axons.
