Artificial neural networks and learning techniques

Pushpendu Kar

doi:10.4018/978-1-4666-9479-8.ch009

Artificial neural networks and learning techniques

Pushpendu Kar

School of Computer Science

Research output: Chapter in Book/Conference proceeding › Book Chapter › peer-review

Abstract

The recent craze for artificial neural networks has spread its roots towards the development of neuroscience, pattern recognition, machine learning and artificial intelligence. The theoretical neuroscience is basically converging towards the basic concept that the brain acts as a complex and decentralized
computer which can perform rigorous calculations in a different approach compared to the conventional digital computers. The motivation behind the study of neural networks is due to their similarity in the structure of the human central nervous system. The elementary processing component of an Artificial
Neural Network (ANN) is called as ‘Neuron’. A large number of neurons interconnected with each other mimic the biological neural network and form an ANN. Learning is an inevitable process that can be used to train an ANN. We can only transfer knowledge to the neural network by the learning procedure.
This chapter presents the detailed concepts of artificial neural networks in addition to some significant aspects on the present research work.

Original language	English
Title of host publication	Handbook of research on advanced computational techniques for simulation-based engineering
Editors	Pijush Samui
Place of Publication	Hershey PA
Publisher	IGI Global Publishing
Chapter	9
Pages	227-251
ISBN (Electronic)	9781466694804
ISBN (Print)	9781466694798, 1466694793
DOIs	https://doi.org/10.4018/978-1-4666-9479-8.ch009
Publication status	Published - 2016

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10.4018/978-1-4666-9479-8.ch009

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title = "Artificial neural networks and learning techniques",

abstract = "The recent craze for artificial neural networks has spread its roots towards the development of neuroscience, pattern recognition, machine learning and artificial intelligence. The theoretical neuroscience is basically converging towards the basic concept that the brain acts as a complex and decentralizedcomputer which can perform rigorous calculations in a different approach compared to the conventional digital computers. The motivation behind the study of neural networks is due to their similarity in the structure of the human central nervous system. The elementary processing component of an ArtificialNeural Network (ANN) is called as {\textquoteleft}Neuron{\textquoteright}. A large number of neurons interconnected with each other mimic the biological neural network and form an ANN. Learning is an inevitable process that can be used to train an ANN. We can only transfer knowledge to the neural network by the learning procedure.This chapter presents the detailed concepts of artificial neural networks in addition to some significant aspects on the present research work.",

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AB - The recent craze for artificial neural networks has spread its roots towards the development of neuroscience, pattern recognition, machine learning and artificial intelligence. The theoretical neuroscience is basically converging towards the basic concept that the brain acts as a complex and decentralizedcomputer which can perform rigorous calculations in a different approach compared to the conventional digital computers. The motivation behind the study of neural networks is due to their similarity in the structure of the human central nervous system. The elementary processing component of an ArtificialNeural Network (ANN) is called as ‘Neuron’. A large number of neurons interconnected with each other mimic the biological neural network and form an ANN. Learning is an inevitable process that can be used to train an ANN. We can only transfer knowledge to the neural network by the learning procedure.This chapter presents the detailed concepts of artificial neural networks in addition to some significant aspects on the present research work.

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BT - Handbook of research on advanced computational techniques for simulation-based engineering

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Artificial neural networks and learning techniques

Abstract

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