scholarly journals An Agent-based Pedestrian Evacuation Modeling from a Rectangular Room Using Cellular Automata and Fuzzy Inference System

2018 ◽  
Vol 5 (4) ◽  
pp. 67-91
Author(s):  
Seyed Mohammad Homayooni ◽  
Parham Pahlevani ◽  
◽  
Keyword(s):  
Cellular Automata ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Evacuation Modeling ◽  
Inference System ◽  
Agent Based ◽  
Pedestrian Evacuation ◽  
Rectangular Room

scholarly journals Development of a Microscopic Fuzzy Inference Based Cellular Automata Model for Pedestrian Flow

2021 ◽  
pp. 1-17
Author(s):  
Ujjal Chattaraj
Keyword(s):  
Cellular Automata ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Field Structure ◽  
Microscopic Model ◽  
Pedestrian Flow ◽  
Inference System ◽  
Cellular Automata Model ◽  
Proposed Model ◽  
Over Time

In this work a microscopic model on pedestrian flow has been proposed. Observed data is used to calibrate and validate the proposed model. The model developed here uses a fuzzy inference system to represent the rules and a force–field structure to represent the repulsive and attractive impacts of objects and goals, respectively in the flow space. The flow space and time are discretized and viewed as discrete quantities. This microscopic model of pedestrian behavior, which essentially models how each pedestrian behaves over time in the flow space, is embedded in a simulation model which is used to simulate situations similar to the ones for which experiments have been done. The results show that the model performs reasonably well.

Syndicated venture capital portfolio companies selection: a fuzzy inference system – agent-based approach

2021 ◽  
pp. 1-16
Author(s):  
S. Ali Hasheminejad ◽  
Morteza Bagherpour ◽  
Siamak Nouri ◽  
Mir Saman Pishvaee
Keyword(s):  
Venture Capital ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Agent Based

A Fuzzy Inference System and Data Mining Toolkit for Agent-Based Simulation in NetLogo

2018 ◽  
pp. 127-149
Author(s):  
Josue-Miguel Flores-Parra ◽  
Manuel Castañón-Puga ◽  
Carelia Gaxiola-Pacheco ◽  
Luis-Enrique Palafox-Maestre ◽  
Ricardo Rosales ◽  
...  
Keyword(s):  
Data Mining ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Agent Based Simulation ◽  
Inference System ◽  
Agent Based

SIMULASI PENGATURAN LAMPU LALU LINTAS MENGGUNAKAN CELLULAR AUTOMATA DAN FUZZY INFERENCE SYSTEM

2015 ◽  
Author(s):  
Septian Nugraha Kudrat ◽  
Yuliant Sibaroni ◽  
Erwin Budi Setiawan
Keyword(s):  
Cellular Automata ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System

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.

Prediction of the Number of Airport Passengers Using Fuzzy C-Means and Adaptive Neuro Fuzzy Inference System

2017 ◽  
Vol 10 (3) ◽  
pp. 280
Author(s):  
Sunarsan Sitohang ◽  
Abba Suganda Girsang ◽  
Suharjito Suharjito
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Fuzzy C Means ◽  
Neuro Fuzzy

ZERO-DAY POLYMORPHIC CYBERATTACKS DETECTION USING FUZZY INFERENCE SYSTEM

2020 ◽  
pp. 8-13
Author(s):  
V. V. Fesokha ◽  
I. Y. Subach ◽  
V. O. Kubrak ◽  
A. V. Mykytiuk ◽  
S. O. Korotaiev
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System
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