Results 1 to 10 of about 15,118 (153)

ANFIS optimized semi-active fuzzy logic controller for magnetorheological dampers [PDF]

Open Engineering, 2016
In this paper, we report on the development of a neuro-fuzzy controller for magnetorheological dampers using an Adaptive Neuro-Fuzzy Inference System or ANFIS.
César Manuel Braz, Barros Rui Carneiro
doaj   +4 more sources

Electricity consumption forecasting using Adaptive Neuro-Fuzzy Inference System (ANFIS) [PDF]

Universal Journal of Electrical and Electronic Engineering, 2019
Universiti Tun Hussein Onn Malaysia (UTHM) is a developing Malaysian Technical University. There is a great development of UTHM since its formation in 1993.
K.G., Tay, Muwafaq, Hassan, W. K., Tiong, Y. Y., Choy   +3 more
core   +4 more sources

Development and Experimental Implementation of Optimized PI-ANFIS Controller for Speed Control of a Brushless DC Motor in Fuel Cell Electric Vehicles

Energies, 2023
This paper compares the performance of different control techniques applied to a high-performance brushless DC (BLDC) motor. The first controller is a classical proportional integral (PI) controller. In contrast, the second one is based on adaptive neuro-
Abdessamad Intidam, Hassan El Fadil, Halima Housny, Zakariae El Idrissi, Abdellah Lassioui, Soukaina Nady, Abdeslam Jabal Laafou   +6 more
doaj   +1 more source

Strategi Implementasi Adaptive Neuro Fuzzy Inference System (ANFIS) pada Kendali Motor Induksi 3 Fase Metode Vektor-Tidak Langsung

Jurnal Elkomika, 2021
ABSTRAK Kendali vektor merupakan solusi terbaik dalam kendali motor induksi untuk meningkatkan karakter dinamis dan efisiensinya. Pada penelitian ini, sebuah kendali kecepatan PID dipadukan dengan Adaptive Neuro Fuzzy Inference System (ANFIS) untuk ...
HANIF HASYIER FAKHRUDDIN, HANDRI TOAR, ERA PURWANTO, HARY OKTAVIANTO, GAMAR BASUKI, RADEN AKBAR NUR APRIYANTO, ABDILLAH AZIZ MUNTASHIR   +6 more
doaj   +1 more source

A Novel Approach for Estimation of Sediment Load in Dam Reservoir With Hybrid Intelligent Algorithms

Frontiers in Environmental Science, 2022
Predicting the amount of sediment in water resource projects is one of the most important measures to be taken, while sediments have an unknown nature in their behavior.
Hojat Karami, Yashar DadrasAjirlou, Changhyun Jun, Sayed M. Bateni, Shahab S. Band, Amir Mosavi, Amir Mosavi, Amir Mosavi, Amir Mosavi, Massoud Moslehpour, Massoud Moslehpour, Kwok-Wing Chau   +11 more
doaj   +1 more source

Comparative Study Analysis of ANFIS and ANFIS-GA Models on Flow of Vehicles at Road Intersections

Applied Sciences, 2023
In the last two decades the efficient traffic-flow prediction of vehicles has been significant in curbing traffic congestions at freeways and road intersections and it is among the many advantages of applying intelligent transportation systems in road ...
Isaac Oyeyemi Olayode, Lagouge Kwanda Tartibu, Frimpong Justice Alex   +2 more
doaj   +1 more source

PSO-ANFIS and ANN Modeling of Propane/Propylene Separation using Cu-BTC Adsorbent [PDF]

Journal of Chemical and Petroleum Engineering, 2019
In this work, an artificial neural network (ANN) model along with a combination of adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) i.e.
Sohrab Fathi, Abbas Rezaei, Majid Mohadesi, Mona Nazari   +3 more
doaj   +3 more sources

STA-APSNFIS: STA-Optimized Adaptive Pre-Sparse Neuro-Fuzzy Inference System for Online Soft Sensor Modeling

IEEE Access, 2020
In complex industrial processes (CIPs), due to technical and economic limitations, key performance indicators (KPIs), especially the chemical content-related KPIs, are often difficult to measure in real time, which hinders the propagation of advanced ...
Jinping Liu, Churong Jiang, Jiezhou He, Zhaohui Tang, Yongfang Xie, Pengfei Xu   +5 more
doaj   +1 more source

Adaptive neuro-fuzzy controller of switched reluctance motor [PDF]

Serbian Journal of Electrical Engineering, 2007
This paper presents an application of adaptive neuro-fuzzy (ANFIS) control for switched reluctance motor (SRM) speed. The ANFIS has the advantages of expert knowledge of the fuzzy inference system and the learning capability of neural networks.
Tahour Ahmed, Abid Hamza, Aissaoui Ghani Abdel   +2 more
doaj   +1 more source

Modeling and design of an automatic generation control for hydropower plants using Neuro-Fuzzy controller

Energy Reports, 2021
This paper presents the modeling, design, and experimental analysis of an Automatic Generation Control (AGC) for a hydropower plant using Adaptive-Neuro-Fuzzy Inference system (ANFIS).
Tilahun Weldcherkos, Ayodeji Olalekan Salau, Aderajew Ashagrie   +2 more
doaj   +1 more source