scholarly journals Measuring System for Impact Point of Arrow using Mamdani Fuzzy Inference System

2012 ◽  
Vol 22 (4) ◽  
pp. 521-526 ◽  
Author(s):  
Jung-Won Yu ◽  
Han-Soo Lee ◽  
Yeong-Sang Jeong ◽  
Sung-Shin Kim
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Measuring System ◽  
Impact Point ◽  
Inference System ◽  
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

Characterization of wear and prediction of wear zone locations on the rake face using Mamdani fuzzy inference system

2016 ◽  
Vol 232 (1) ◽  
pp. 78-89 ◽  
Author(s):  
PNL Pavani ◽  
CLVRSV Prasad ◽  
K Ramji ◽  
SV Ramana
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Cutting Tools ◽  
Fuzzy Model ◽  
Cutting Edge ◽  
Average Error ◽  
High Wear Resistance ◽  
Rake Face ◽  
Inference System ◽  
Mamdani Fuzzy Inference System

In order to improve the performance of the cutting tool, third-generation tools with multi-layered nanocoatings on the rake face are used. During machining, the chip–tool interactions depict that although the tool wear on the rake face is located in the close proximity of the cutting edge, that is, within 800 µm, all the commercially available cutting tools have the coatings on the entire rake face. Taking into account the tribological properties required by the rake face close to the cutting edge, that is, high wear resistance and low friction, this study makes an attempt to identify, characterize and locate the actual wear zones/regions in terms of hard and soft zones in the chip contact area of tungsten carbide (WC) inserts close to the cutting edge in turning. Mamdani fuzzy inference system model was developed, trained with the sample experimental data and tested with the test data. The simulated results showed that the average error values of edge chipping (in X- and Y-directions), nose damage and crater wear (in X- and Y-directions) are about 2.37%, 3.01%, 2.86%, 2.66% and 1.89%, respectively. The fuzzy model developed in this study showed remarkable prediction of the wear zone locations and is also helpful for the researchers to decide the type of coating (hard and soft) along the specified zones for reducing the cost of production.

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