Introduction

Artificial neural network is a mathematical model of human brain, which can be trained to perform a specific task based on available knowledge base. Its design loosely follows neural structure of a human brain. Human brain excels the conventional computers in three broad areas: pattern recognition, associative recalling, and learning.
Artificial Neural network is composed of three layers:
• Inputs
• one or many hidden layers
• output layer
Hidden and output layers include the combination of weights, biases, and transfer functions. The transfer functions are linear or non-linear algebraic functions. When an input is presented to the network, then weights and biases are adjusted so that a desired output is obtained. Once a neural network is trained to a satisfactory level, it can be used on generic data.

Structure of the Human Brain

The human brain is one of the most complicated and poorly understood things. It contains approximately ten million basic units, called neurons. The neuron is the basic unit of the brain and is stand-alone analogue logical processing unit. The neuron accepts many inputs, which are added up in some fashion. If enough active inputs are received at once, then the neuron will be activated and “fired”, if not, then the neuron will remain in its “inactive” state.

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