Study of Artificial Neural Network

TLDR: Although the working principles and simple set of rules of artificial neuron looks like nothing special the full potential and calculation power of these models come to life when the authors start to interconnect them into artificial neural networks.

Abstract: Sonu, Ravi Parkash Bhokal* Dept. of Mathematics, M.D.U. Rohtak Introduction:An Artificial Neural Network (ANN) is a mathematical model that tries to simulate the structure and functionalities of biological neural networks. Basic building block of every artificial neural network is artificial neuron, that is, a simple mathematical model (function). Such a model has three simple sets of rules: multiplication, summation and activation. At the entrance of artificial neuron the inputs are weighted what means that every input value is multiplied with individual weight. In the middle section of artificial neuron is sum function that sums all weighted inputs and bias. At the exit of artificial neuron the sum of previously weighted inputs and bias is passing trough activation function that is also called transfer function. Although the working principles and simple set of rules of artificial neuron looks like nothing special the full potential and calculation power of these models come to life when we start to interconnect them into artificial neural networks (Figure 1) These artificial neural networks use simple fact that complexity can grown out of merely few basic and simple rules.