Comprehensive Modelling of ANN

Comprehensive Modelling of ANN

Meghna Babubhai Patel (Ganpat University, India), Jagruti N. Patel (Ganpat University, India) and Upasana M. Bhilota (Ganpat University, India)
Copyright: © 2022 |Pages: 10
DOI: 10.4018/978-1-6684-2408-7.ch002
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Abstract

An artificial neural network (ANN) is an information processing modelling of the human brain inspired by the way biological nervous systems behave. There are about 100 billion neurons in the human brain. Each neuron has a connection point between 1,000 and 100,000. The key element of this paradigm is the novel structure of the information processing system. In the human brain, information is stored in such a way as to be distributed, and we can extract more than one piece of this information when necessary from our memory in parallel. We are not mistaken when we say that a human brain is made up of thousands of very powerful parallel processors. It is composed of a large number of highly interconnected processing elements (neurons) working in union to solve specific problems. ANN, like people, learns by example. The chapter includes characteristics of artificial neural networks, structure of ANN, elements of artificial neural networks, pros and cons of ANN.
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Introduction

Features of Artificial Neural Network

  • It’s an impartially applied scientific model.

  • Its contains vast figure of interrelated handling components named neurons to do all operations.

  • Information put in storage in the neurons are basically the weighted linkage of neurons.

  • The input signals reach at the processing components through associates and attaching masses.

  • It has the capability to study, remember and simplify from the given data by suitable assignment and adjustment of weights.

  • The mutual activities of the neurons define its computational power, and no single neuron transmits explicit data.

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