| 000 | 03207nam a22005295i 4500 | ||
|---|---|---|---|
| 001 | 978-981-13-2640-0 | ||
| 003 | DE-He213 | ||
| 005 | 20220801214325.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 180925s2019 si | s |||| 0|eng d | ||
| 020 |
_a9789811326400 _9978-981-13-2640-0 |
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| 024 | 7 |
_a10.1007/978-981-13-2640-0 _2doi |
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| 050 | 4 | _aTK5101-5105.9 | |
| 072 | 7 |
_aTJK _2bicssc |
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| 072 | 7 |
_aTEC041000 _2bisacsh |
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| 072 | 7 |
_aTJK _2thema |
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| 082 | 0 | 4 |
_a621.382 _223 |
| 100 | 1 |
_aAzizi, Aydin. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _937692 |
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| 245 | 1 | 0 |
_aApplications of Artificial Intelligence Techniques in Industry 4.0 _h[electronic resource] / _cby Aydin Azizi. |
| 250 | _a1st ed. 2019. | ||
| 264 | 1 |
_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2019. |
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| 300 |
_aXII, 61 p. 50 illus., 34 illus. in color. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 490 | 1 |
_aSpringerBriefs in Applied Sciences and Technology, _x2191-5318 |
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| 505 | 0 | _aIntroduction -- Modern Manufacturing -- RFID Network Planning -- Hybrid Artificial Intelligence Optimization Technique -- Implementation. | |
| 520 | _aThis 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 |
_aTelecommunication. _910437 |
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| 650 | 0 |
_aArtificial intelligence. _93407 |
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| 650 | 0 |
_aIndustrial Management. _95847 |
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| 650 | 1 | 4 |
_aCommunications Engineering, Networks. _931570 |
| 650 | 2 | 4 |
_aArtificial Intelligence. _93407 |
| 650 | 2 | 4 |
_aIndustrial Management. _95847 |
| 710 | 2 |
_aSpringerLink (Online service) _937693 |
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| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9789811326394 |
| 776 | 0 | 8 |
_iPrinted edition: _z9789811326417 |
| 830 | 0 |
_aSpringerBriefs in Applied Sciences and Technology, _x2191-5318 _937694 |
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| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-981-13-2640-0 |
| 912 | _aZDB-2-ENG | ||
| 912 | _aZDB-2-SXE | ||
| 942 | _cEBK | ||
| 999 |
_c76220 _d76220 |
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