Modeling the initiation of sediment motion under a wide range of flow conditions using a Geno-Mamdani Fuzzy Inference System method

2020 ◽  
Vol 35 (5) ◽  
pp. 467-483
Author(s):  
Hussein Bizimana ◽  
Abdüsselam Altunkaynak
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Flow Conditions ◽  
Inference System ◽  
System Method ◽  
Wide Range ◽  
Mamdani Fuzzy Inference System

scholarly journals Mamdani fuzzy inference system for mapping water quality level of biofloc ponds in catfish cultivation

2020 ◽  
Vol 8 (2) ◽  
pp. 84-88
Author(s):  
Herryawan Pujiharsono ◽  
Danny Kurnianto
Keyword(s):  
Water Quality ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Water Quality Monitoring ◽  
Growth Conditions ◽  
Fish Growth ◽  
Quality Level ◽  
Inference System ◽  
The Government ◽  
Mamdani Fuzzy Inference System

The government has launched a program to increase the production of catfish by using biofloc ponds. The biofloc ponds can maintain the quality of water biologically to maximize the growth of fish. However, the level of water quality monitoring is generally only divided into good or bad categories so that it cannot represent the condition of fish growth. Therefore, this study aims to get the level of water quality (0–100 %) using the Mamdani fuzzy inference system (FIS) algorithm based on pH, temperature, and dissolved oxygen (DO). The level of water quality was correlated based on catfish growth conditions. The results showed that the range of values of the water quality level for each condition of catfish growth was 100 % for normal-living fish, 83–99 % for stunted fish growth, and < 83% for threatened fish. The FIS algorithm had 89.92 % of accuracy.

scholarly journals AN ALGORITHM OF FUZZY INFERENCE SYSTEM FOR ISAR IMAGE CLASSIFICATION

2017 ◽  
Vol 2 ◽  
pp. 154
Author(s):  
Krasimir Slavyanov ◽  
Chavdar Minchev
Keyword(s):  
Neural Network ◽  
Image Classification ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Information Analysis ◽  
Simulation Experiments ◽  
Inference System ◽  
Classification Procedure ◽  
Neural Network Simulation ◽  
Mamdani Fuzzy Inference System

This article offers an original ISAR image classification procedure based on Mamdani fuzzy inference system (FIS) dedicated to compute multiple results each from different type of analyzing criteria. The modeling and information analysis of the FIS are developed to draw a general conclusion from several results each produced by classification from neural network. Simulation experiments are carried out in MATLAB environment.

scholarly journals Live Migration of Virtual Machines Using a Mamdani Fuzzy Inference System

2022 ◽  
Vol 71 (2) ◽  
pp. 3019-3033
Author(s):  
Tahir Alyas ◽  
Iqra Javed ◽  
Abdallah Namoun ◽  
Ali Tufail ◽  
Sami Alshmrany ◽  
...  
Keyword(s):  
Fuzzy Inference System ◽  
Virtual Machines ◽  
Fuzzy Inference ◽  
Live Migration ◽  
Inference System ◽  
Mamdani Fuzzy Inference System

Real-Time Control of a Rotary Inverted Pendulum using Robust LQR-based ANFIS Controller

2018 ◽  
Vol 19 (3-4) ◽  
pp. 379-389 ◽  
Author(s):  
Ishan Chawla ◽  
Ashish Singla
Keyword(s):  
Fuzzy Inference System ◽  
Inverted Pendulum ◽  
Fuzzy Inference ◽  
Sliding Mode ◽  
Linear Quadratic Regulator ◽  
Linear Quadratic ◽  
Inference System ◽  
Wide Range ◽  
Lqr Controller ◽  
Rotary Inverted Pendulum

AbstractFrom the last five decades, inverted pendulum (IP) has been considered as a benchmark problem in the control literature due to its inherit nature of instability, non-linearity and underactuation. Its applicability in wide range of practical systems, demands the need of a robust controller. It is found in the literature that wide range of controllers had been tested on this problem, out of which the most robust being sliding mode controller while the most optimal being linear quadratic regulator (LQR) controller. The former has a problem of discontinuity and chattering, while the latter lacks the property of robustness. To address the robustness issue in LQR controller, this paper proposes a novel robust LQR-based adaptive neural based fuzzy inference system controller, which is a hybrid of LQR and fuzzy inference system. The proposed controller is designed and implemented on rotary inverted pendulum. Further, to validate the robustness of proposed controller to parametric uncertainties, pendulum mass is varied. Simulation and experimental results show that as compared to LQR controller, the proposed controller is robust to variations in pendulum mass and has shown satisfactory performance.

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