02282naa a22003138i 4500 5334 5334 00005334 FR-PaOEC 20211006062618.0 a o d i cr || |||m|n|| 190601s2018 ||| o i|0| 0 eng d (FR-PaOEC) FR-PaOEC ES-MaONT Oduro-Gyimah, Francis Kwabena 3381 Application of CANFIS model in the prediction of multiple-input telecommunication network traffic / Francis Kwabena Oduro-Gyimah and Kwame Osei Boateng Geneva : International Telecommunication Union, 2018. 9 p. texto (visual) isbdcontent electrónico isbdmedia recurso en línea rdacarrier 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%. Tecnologías habilitadoras digitales 18 Science and Technology Boateng, Kwame Osei 3382 4843 ITU Journal: ICT Discoveries Vol. 2018, no. 2, p. 73-81 2018:2<73 2616-8375 itu-ilibrary.org https://www.itu.int/dms_pub/itu-s/opb/journal/S-JOURNAL-ICTS.V1I2-2018-10-PDF-E.pdf Acceso al documento 0 pdf ART udc 0 0 0 3 1 6284 01 01 online 2019-09-12 0 1000020175583 2019-09-12 00:00:00 2019-09-12 ART pdf