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Applications of artificial intellige...

Azizi, Aydin.

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Applications of artificial intelligence techniques in Industry 4.0

Record Type:	Electronic resources : Monograph/item
Title/Author:	Applications of artificial intelligence techniques in Industry 4.0/ by Aydin Azizi.
Author:	Azizi, Aydin.
Published:	Singapore :Springer Singapore : : 2019.,
Description:	xii, 61 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- Modern Manufacturing -- RFID Network Planning -- Hybrid Artificial Intelligence Optimization Technique -- Implementation.
Contained By:	Springer eBooks
Subject:	Artificial intelligence - Industrial applications. -
Online resource:	https://doi.org/10.1007/978-981-13-2640-0
ISBN:	9789811326400

Applications of artificial intelligence techniques in Industry 4.0
Azizi, Aydin.

Applications of artificial intelligence techniques in Industry 4.0[electronic resource] /by Aydin Azizi. - Singapore :Springer Singapore :2019. - xii, 61 p. :ill., digital ;24 cm. - SpringerBriefs in applied sciences and technology,2191-530X. - SpringerBriefs in applied sciences and technology..

Introduction -- Modern Manufacturing -- RFID Network Planning -- Hybrid Artificial Intelligence Optimization Technique -- Implementation.

This book is to presents and evaluates a way of modelling and optimizing nonlinear RFID Network Planning (RNP) problems using artificial intelligence techniques. It uses Artificial Neural Network models (ANN) to bind together the computational artificial intelligence algorithm with knowledge representation an efficient artificial intelligence paradigm to model and optimize RFID networks. This effort leads to proposing a novel artificial intelligence algorithm which has been named hybrid artificial intelligence optimization technique to perform optimization of RNP as a hard learning problem. This hybrid optimization technique consists of two different optimization phases. First phase is optimizing RNP by Redundant Antenna Elimination (RAE) algorithm and the second phase which completes RNP optimization process is Ring Probabilistic Logic Neural Networks (RPLNN) The hybrid paradigm is explored using a flexible manufacturing system (FMS) and the results are compared with well-known evolutionary optimization technique namely Genetic Algorithm (GA) to demonstrate the feasibility of the proposed architecture successfully.

ISBN: 9789811326400

Standard No.: 10.1007/978-981-13-2640-0doiSubjects--Topical Terms:

653318
Artificial intelligence
--Industrial applications.

LC Class. No.: TA347.A78 / A959 2019

Dewey Class. No.: 006.3

Applications of artificial intelligence techniques in Industry 4.0
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100 1 $a Azizi, Aydin. $3 3380105
245 1 0 $a Applications of artificial intelligence techniques in Industry 4.0 $h [electronic resource] / $c by Aydin Azizi.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2019.
300 $a xii, 61 p. : $b ill., digital ; $c 24 cm.
490 1 $a SpringerBriefs in applied sciences and technology, $x 2191-530X
505 0 $a Introduction -- Modern Manufacturing -- RFID Network Planning -- Hybrid Artificial Intelligence Optimization Technique -- Implementation.
520 $a This book is to presents and evaluates a way of modelling and optimizing nonlinear RFID Network Planning (RNP) problems using artificial intelligence techniques. It uses Artificial Neural Network models (ANN) to bind together the computational artificial intelligence algorithm with knowledge representation an efficient artificial intelligence paradigm to model and optimize RFID networks. This effort leads to proposing a novel artificial intelligence algorithm which has been named hybrid artificial intelligence optimization technique to perform optimization of RNP as a hard learning problem. This hybrid optimization technique consists of two different optimization phases. First phase is optimizing RNP by Redundant Antenna Elimination (RAE) algorithm and the second phase which completes RNP optimization process is Ring Probabilistic Logic Neural Networks (RPLNN) The hybrid paradigm is explored using a flexible manufacturing system (FMS) and the results are compared with well-known evolutionary optimization technique namely Genetic Algorithm (GA) to demonstrate the feasibility of the proposed architecture successfully.
650 0 $a Artificial intelligence $x Industrial applications. $3 653318
650 1 4 $a Communications Engineering, Networks. $3 891094
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 890894
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856 4 0 $u https://doi.org/10.1007/978-981-13-2640-0
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