Applications of artificial intelligence techniques in Industry 4.0 /

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Bibliographic Details
Author / Creator:Azizi, Aydin, author.
Imprint:Singapore : Springer, 2019.
Description:1 online resource.
Language:English
Series:SpringerBriefs in applied sciences and technology
SpringerBriefs in applied sciences and technology.
Subject:Artificial intelligence -- Industrial applications.
COMPUTERS / General.
Artificial intelligence -- Industrial applications.
Artificial intelligence.
Engineering: general.
Communications engineering / telecommunications.
Communications Engineering, Networks.
Artificial Intelligence (incl. Robotics).
Engineering Economics, Organization, Logistics, Marketing.
Electronic books.
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11745868
Hidden Bibliographic Details
ISBN:9789811326400
9811326401
9789811326394
9811326398
9789811326394
9789811326417
981132641X
Digital file characteristics:text file PDF
Notes:Includes bibliographical references.
Online resource; title from PDF title page (EBSCO, viewed September 28, 2018).
Summary: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.
Other form:Printed edition: 9789811326394
Printed edition: 9789811326417
Standard no.:10.1007/978-981-13-2640-0