語系:
繁體中文
English
說明(常見問題)
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Applications of Artificial Intellige...
~
SpringerLink (Online service)
Applications of Artificial Intelligence Techniques in Industry 4.0
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Applications of Artificial Intelligence Techniques in Industry 4.0/ by Aydin Azizi.
作者:
Azizi, Aydin.
面頁冊數:
XII, 61 p. 50 illus., 34 illus. in color.online resource. :
Contained By:
Springer Nature eBook
標題:
Electrical engineering. -
電子資源:
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. - 1st ed. 2019. - XII, 61 p. 50 illus., 34 illus. in color.online resource. - 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:
596380
Electrical engineering.
LC Class. No.: TK1-9971
Dewey Class. No.: 621.382
Applications of Artificial Intelligence Techniques in Industry 4.0
LDR
:02631nam a22003975i 4500
001
1008806
003
DE-He213
005
20200705155411.0
007
cr nn 008mamaa
008
210106s2019 si | s |||| 0|eng d
020
$a
9789811326400
$9
978-981-13-2640-0
024
7
$a
10.1007/978-981-13-2640-0
$2
doi
035
$a
978-981-13-2640-0
050
4
$a
TK1-9971
072
7
$a
TJK
$2
bicssc
072
7
$a
TEC041000
$2
bisacsh
072
7
$a
TJK
$2
thema
082
0 4
$a
621.382
$2
23
100
1
$a
Azizi, Aydin.
$e
author.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1302660
245
1 0
$a
Applications of Artificial Intelligence Techniques in Industry 4.0
$h
[electronic resource] /
$c
by Aydin Azizi.
250
$a
1st ed. 2019.
264
1
$a
Singapore :
$b
Springer Singapore :
$b
Imprint: Springer,
$c
2019.
300
$a
XII, 61 p. 50 illus., 34 illus. in color.
$b
online resource.
336
$a
text
$b
txt
$2
rdacontent
337
$a
computer
$b
c
$2
rdamedia
338
$a
online resource
$b
cr
$2
rdacarrier
347
$a
text file
$b
PDF
$2
rda
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
Electrical engineering.
$3
596380
650
0
$a
Artificial intelligence.
$3
559380
650
0
$a
Engineering economics.
$3
1253802
650
0
$a
Engineering economy.
$3
632434
650
1 4
$a
Communications Engineering, Networks.
$3
669809
650
2 4
$a
Artificial Intelligence.
$3
646849
650
2 4
$a
Engineering Economics, Organization, Logistics, Marketing.
$3
669171
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
776
0 8
$i
Printed edition:
$z
9789811326394
776
0 8
$i
Printed edition:
$z
9789811326417
830
0
$a
SpringerBriefs in Applied Sciences and Technology,
$x
2191-530X
$3
1253575
856
4 0
$u
https://doi.org/10.1007/978-981-13-2640-0
912
$a
ZDB-2-ENG
912
$a
ZDB-2-SXE
950
$a
Engineering (SpringerNature-11647)
950
$a
Engineering (R0) (SpringerNature-43712)
筆 0 讀者評論
多媒體
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼[密碼必須為2種組合(英文和數字)及長度為10碼以上]
登入