Media synchronization in multimedia Web using a neuro-fuzzy framework

2000 ◽  
Vol 18 (2) ◽  
pp. 168-183 ◽  
Author(s):  
Z. Ali ◽  
A. Ghafoor ◽  
C.S.G. Lee
Keyword(s):  
Media Synchronization ◽  
Neuro Fuzzy ◽  
Fuzzy Framework

A New Self-Regulated Neuro-Fuzzy Framework for Classification of EEG Signals in Motor Imagery BCI

2018 ◽  
Vol 26 (3) ◽  
pp. 1485-1497 ◽  
Author(s):  
Aysa Jafarifarmand ◽  
Mohammad Ali Badamchizadeh ◽  
Sohrab Khanmohammadi ◽  
Mohammad Ali Nazari ◽  
Behzad Mozaffari Tazehkand
Keyword(s):  
Motor Imagery ◽  
Eeg Signals ◽  
Neuro Fuzzy ◽  
Fuzzy Framework

scholarly journals A SUMO Based Simulation Framework for Intelligent Traffic Management System

2020 ◽  
pp. 1-5
Author(s):  
Shamim Akhter ◽  
◽  
Md. Nurul Ahsan ◽  
Shah Jafor Sadeek Quaderi ◽  
Md. Abdullah Al Forhad ◽  
...  
Keyword(s):  
Traffic Management ◽  
Management System ◽  
Road Traffic ◽  
Fuzzy Model ◽  
Road Segment ◽  
World Population ◽  
Dijkstra Algorithm ◽  
Neuro Fuzzy ◽  
Traffic Management System ◽  
Fuzzy Framework

Continuous increments in world population demands transportation with essential vehicle facilities and directly effect on road traffic volume or congestion, mostly in metropolitan cities, and thus it needs significant investigation, analysis, and maintenance. In these regards, an Intelligent Traffic Management System (ITMS) with a Deep-Neuro-Fuzzy model was proposed and implemented. Dijkstra algorithm is used to select optimum path from source to destination on the basis of calculated road segment weights from Deep-Neuro-Fuzzy framework. However, Deep-Neuro-Fuzzy framework needs some comprehensive analysis, other means some simulation or emulation, and etc, to proof the efficiency and workability of the model. In this paper, we are going to explore the Deep-Neuro-Fuzzy model in pragmatic style with an open-source traffic simulation model (SUMO) and helps to explore traffic-related issues including route choice, simulate traffic light or vehicular communication, etc in our ITMS. In addition, a new GUI is developed to control the simulation input attributes and presents the feedbacks into the traffic flow in SUMO environment. Results highlight that the proposed SUMO model can realistically simulate ITMS based on the road segment weights from Deep-Neuro-Fuzzy model. Different built-in routing algorithms are also used to proof the workability of this model.

Conventional and neuro-fuzzy framework for diagnosis and therapy of cardiovascular disease

2013 ◽  
Vol 9 (3) ◽  
Author(s):  
Okure U. Obot ◽  
Faith-Michael Uzoka ◽  
Oluwole C. Akinyokun ◽  
Joseph J. Andy
Keyword(s):  
Cardiovascular Disease ◽  
Diagnosis And Therapy ◽  
Neuro Fuzzy ◽  
Fuzzy Framework

A neuro-fuzzy framework for inferencing

2002 ◽  
Vol 15 (2) ◽  
pp. 247-261 ◽  
Author(s):  
Sukumar Chakraborty ◽  
Kuhu Pal ◽  
Nikhil R. Pal
Keyword(s):  
Neuro Fuzzy ◽  
Fuzzy Framework

THE SETTINGS ALGORITHM OF A NEURO-FUZZY PI-CONTROLLER

2020 ◽  
Vol 12 (2) ◽  
Author(s):  
Atamurat Mambetov ◽  
Rasul Beglerbekov ◽  
Hurliman Sultanova
Keyword(s):  
Pi Controller ◽  
Neuro Fuzzy ◽  
Fuzzy Pi Controller

Aplikasi Peramalan Kebutuhan Beban Listrik Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS)

2017 ◽  
Vol 3 (1) ◽  
pp. 36-48
Author(s):  
Erwan Ahmad Ardiansyah ◽  
Rina Mardiati ◽  
Afaf Fadhil
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

Prakiraan atau peramalan beban listrik dibutuhkan dalam menentukan jumlah listrik yang dihasilkan. Ini menentukan  agar tidak terjadi beban berlebih yang menyebabkan pemborosan atau kekurangan beban listrik yang mengakibatkan krisis listrik di konsumen. Oleh karena itu di butuhkan prakiraan atau peramalan yang tepat untuk menghasilkan energi listrik. Teknologi softcomputing dapat digunakan  sebagai metode alternatif untuk prediksi beban litrik jangka pendek salah satunya dengan metode  Adaptive Neuro Fuzzy Inference System pada penelitian tugas akhir ini. Data yang di dapat untuk mendukung penelitian ini adalah data dari APD PLN JAWA BARAT yang berisikan laporan data beban puncak bulanan penyulang area gardu induk majalaya dari januari 2011 sampai desember 2014 sebagai data acuan dan data aktual januari-desember 2015. Data kemudian dilatih menggunakan metode ANFIS pada software MATLAB versi b2010. Dari data hasil pelatihan data ANFIS kemudian dilakukan perbandingan dengan data aktual dan data metode regresi meliputi perbandingan anfis-aktual, regresi-aktual dan perbandingan anfis-regresi-aktual. Dari perbandingan disimpulkan bahwa data metode anfis lebih mendekati data aktual dengan rata-rata 1,4%, menunjukan prediksi ANFIS dapat menjadi referensi untuk peramalan beban listrik dimasa depan.

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