Application of CANFIS model in the prediction of multiple-input telecommunication network traffic / (Record no. 5334)

MARC details
000 -LEADER
fixed length control field 02282naa a22003138i 4500
001 - CONTROL NUMBER
control field 00005334
003 - CONTROL NUMBER IDENTIFIER
control field FR-PaOEC
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20211006062618.0
006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS
fixed length control field a o d i
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
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008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 190601s2018 ||| o i|0| 0 eng d
035 ## - SYSTEM CONTROL NUMBER
System control number (FR-PaOEC)
040 ## - CATALOGING SOURCE
Original cataloging agency FR-PaOEC
Transcribing agency ES-MaONT
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Oduro-Gyimah, Francis Kwabena
9 (RLIN) 3381
245 10 - TITLE STATEMENT
Title Application of CANFIS model in the prediction of multiple-input telecommunication network traffic /
Statement of responsibility, etc. Francis Kwabena Oduro-Gyimah and Kwame Osei Boateng
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Geneva :
Name of publisher, distributor, etc. International Telecommunication Union,
Date of publication, distribution, etc. 2018.
300 ## - PHYSICAL DESCRIPTION
Extent 9 p.
336 ## - CONTENT TYPE
Content type term texto (visual)
Source isbdcontent
337 ## - MEDIA TYPE
Media type term electrónico
Source isbdmedia
338 ## - CARRIER TYPE
Carrier type term recurso en línea
Source rdacarrier
520 3# - SUMMARY, ETC.
Summary, etc. Telecommunication network traffic prediction is an important approach that ensure efficient network planning and management. Telecommunication network traffic is univariate and prediction models have mostly been concentrated on single-input and single-output traffic. This study proposes a new approach, the multiple-input multiple-output Coactive Neuro-Fuzzy Inference System (CANFIS) model to predict a five time span univariate hourly, daily, weekly, monthly and quarterly time series of 3G downlink traffic simultaneously. In the modelling process several parameters were used in the configuration of the network. The best model for predicting five-input telecommunication traffic was CANFIS (5-2-5) which employed a Bell membership function, Axon transfer function and Momentum learning rule and the membership function per input of 2. The performance of the model was evaluated by comparing the predicted traffic with actual traffic obtained from a 3G network operator and the results indicate a minimum accuracy measure value of MSE = 0.000486, NRMSE = 0.01120 and percent error = 12.33%.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Tecnologías habilitadoras digitales
9 (RLIN) 18
653 #4 - INDEX TERM--UNCONTROLLED
Uncontrolled term Science and Technology
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Boateng, Kwame Osei
9 (RLIN) 3382
773 0# - HOST ITEM ENTRY
Host Biblionumber 4843
Title ITU Journal: ICT Discoveries
Related parts Vol. 2018, no. 2, p. 73-81
Enumeration and first page 2018:2<73
International Standard Serial Number 2616-8375
856 40 - ELECTRONIC LOCATION AND ACCESS
Host name itu-ilibrary.org
Uniform Resource Identifier https://www.itu.int/dms_pub/itu-s/opb/journal/S-JOURNAL-ICTS.V1I2-2018-10-PDF-E.pdf
Link text Acceso al documento
Nonpublic note Abierto
Electronic format type pdf
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Artículos
Source of classification or shelving scheme Universal Decimal Classification
Holdings
Withdrawn status Lost status Damaged status Not for loan Collection code Koha itemnumber Home library Current library Shelving location Date acquired Total Checkouts Barcode Date last seen Price effective from Koha item type Public note
      Acceso libre online Colección digital 6284 CDO CDO   12/09/2019   1000020175583 12/09/2019 12/09/2019 Artículos pdf
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