Mamdani Inference System Research Articles

Fuzzy Logic Harmony in Water: Mamdani Inference System Applied to Evaluate Pristine Pond Water Quality

Aquatic ecosystems that are subject to urbanization and environmental changes, such as the Kapaleeswarar and Chitrakulam tanks, depend on evaluating water quality. Their complicated data present challenges for conventional approaches. The usefulness of the Mamdani fuzzy inference system in determining the water quality in these tanks is investigated in this work. It creates a comprehensive assessment based on subject-matter expertise by handling ambiguous descriptors with linguistic variables and fuzzy sets. The system’s procedures for implementation are described in detail, with an emphasis on how well they can manage interrelated variables. The study shows how well the system measures the water quality in tanks and suggests ways to improve it. Tank evaluation that incorporates the Mamdani system encourages comprehensive resource management and cultural preservation.

Design and Implementation of Fuzzy Logic for Obstacle Avoidance in Differential Drive Mobile Robot

Autonomous mobile robots based on wheel drive are widely used in various applications. The differential drive mobile robot (DDMR) is one type with wheel drive. DDMR uses one actuator to move each wheel on the mobile robot. Autonomous capabilities are needed to avoid obstacles around the DDMR. This paper presents implementing a fuzzy logic algorithm for obstacle avoidance at a low cost (DDMR). The fuzzy logic algorithm input is obtained from three ultrasonic sensors installed in front of the DDMR with an angle difference between the sensors of 45$^0$. Distance information from the ultrasonic sensors is used to regulate the speed of the right and left motors of the DDMR. Based on the test results, the Mamdani inference system using the fuzzy logic algorithm was successfully implemented as an obstacle avoidance algorithm. The speed values of the right and left DDMR wheels produce values according to the rules created in the Mamdani inference system. DDMR managed to pass through a tunnel-shaped environment and reach its goal without hitting any obstacles around it. The average speed produced by DDMR in reaching the goal is 4.91 cm/s.

Control and management of water quality for Nile tilapia fish in net tanks based on fuzzy modeling

Being classified as one of the most important species for fish farming, the Nile tilapia has seen a huge increase in breeding worldwide. As in any cultural medium, dealing with the quality of the medium in which it is grown guarantees a large part of the success of the process, being of equal importance, in this case, the quality of the water. Taking advantage of existing mathematical models, humans were able to measure and design best practices in virtually all areas, pointing to its great functionality, this article used the Fuzzy logic mathematical model together with Mamdani inference to analyze water quality scenarios and their consequences, various environments, variables, capable of directly affecting fish farming. The purpose was to use the MatLab scientific software to cross these variables with the possible output scenarios, facilitating the producer's decision-making. As a result of the research, it was possible to develop an algorithm to be embedded in a mobile application in the future with fuzzy mathematical modeling, with a Mamdani inference system for management and control of water quality in Nile Tilapia fish farming. The same will be made available to these breeders, since it has a structure of rules, aiming at the delivery of scientific information that collaborates with the best cultivation practices, improving production and profitability, through decision support to fish farmers.

Analysis of Faculty Teaching Performance Based On Student Feedback Using Fuzzy Mamdani Inference System

The objective of this study was to develop an Inference System based on the Fuzzy Mamdani system for assessing the teaching skills of faculty members in academic or educational institutions. Nowadays, many academic institutions have adopted web-based methods to gather students' feedback on faculty members' teaching effectiveness. Evaluating faculty performance in teaching activities is crucial for establishing a more equitable academic environment. The primary goal of assessing faculty performance is to identify areas of strength and weakness in their professional development. This application of soft computing, specifically the Fuzzy Mamdani Inference System (FMIS), in evaluating faculty teaching performance can be valuable for organizational management in assessing faculty capabilities and their impact on student outcomes.

Development and Tuning of a Nonlinear Six DOF Model and Controllers for a Large UAV

This paper presents development of non-linear six-degree-of-freedom model for a large UAV and simulation of its dynamics during its landing phase. A non-linear six-degree-of-freedom aircraft model was developed using the block available in the Aerosim Aeronautical Simulation Blockset in MATLAB / SIMULINK environment. The aircraft parameter data for a large UAV was taken. The developed model could be used for simulating the dynamics of the UAV with single piston engine and fixed pitch propeller. The following controllers were tuned using the above model to simulate its landing phase. ‘Bank to Aileron’, ‘Airspeed error to Pitch command’, ‘Pitch error to Elevator deflection’, ‘Engine rpm error to Throttle’. The first approach was by conventional techniques using PI and PID controllers. In the next stage fuzzy logic controllers were designed for the above cases. All fuzzy controllers were designed using Mamdani inference system.

Building a Cost Modeling System using Fuzzy Logic for Sugar Industry

To understand the effects of cost factors on the overall production cost, the study's goal was todevelop a fuzzy logic-based cost modelling system for the sugar sector. The information is gathered fromsugar factories in Pakistan. Utilising a multistage fuzzy inference approach, the model is created. To analysethe cost of producing sugar, the model is verified using cost factors including the cost of raw materials, thecost of labour, and the cost of distribution. The main goal of the study was to ascertain how theseuncontrollable cost factors affected the price of producing sugar. The sub-cost components were used toanalyse the cost variables independently. For each cost variable, a different fuzzy inference technique wasused to interpret their response. Then a complete Mamdani inference system for manufacturing cost wascreated. The final inference system's input was derived from the results of the sub inference systems. Inorder to design the system to assess the effects of specified cost drivers on the production cost, a total ofthree input variables, one output variable, and twenty-seven if-then rules were established. The createdfuzzy logic-based system can assess the cost of producing sugar while taking uncertainty into consideration.As a result, the created system's offer of a cost estimating model that makes it easier to choose outcomesthat are cost-effective is a major contribution.

Expert System for Diagnosing Airborne Diseases Airborne Diseases in Humans with Fuzzy Logic Mamdani Method

<p>People in today's information age are increasingly demanding all kinds of information to be presented quickly. One of them is information in the medical field, in this case information about the diagnosis of a disease which is usually in the form of an expert system. Various methods are used in expert systems, one of which is Mamdani fuzzy logic or better known as the Mamdani inference system. The use of fuzzy logic in diagnosing this disease does not require numbers as input.</p><p>The Mamdani fuzzy logic expert system in this study is specifically used to diagnose infectious diseases in humans, namely: whooping cough or pertussis, measles, diphtheria, mumps, meningitis, tuberculosis, variola, and varicella. This system diagnoses diseases based on inputting the characteristics or symptoms suffered. The diagnosis result is a number of possible diagnoses for each disease. The closer the number to 1 (one), the higher the probability of contracting one of the diseases mentioned above.</p><p>The system that has been created can already be used to detect diseases according to the characteristics or symptoms entered. However, this system is only a research and the results cannot be used as a reference for real disease diagnosis.</p><p> </p>

COMPARATIVE STUDY OF CENTROID DEFUZZIFICATION AND MAXIMUM DEFUZZIFIER MODEL MEAN OF MAXIMA ALGORITHM FOR CORONARY ARTERY DISEASE DIAGNOSIS

Objective: Cardiovascular, especially coronary artery disease (CAD), is the number one cause of death globally. Every year, more people die from cardiovascular disease than any other disease. There are several factors and symptoms of coronary artery disease that are easily recognized. To find out the severity of CAD, a diagnosis of CAD is needed to facilitate medical treatment. This study aims to explain the diagnostic process for CAD using a fuzzy system and to determine the accuracy of the fuzzy system. Design and method: This study uses a fuzzy system to diagnose the severity of coronary artery disease. Input variables used in the study were gender, age, pulse, systolic blood pressure, cholesterol, blood sugar, triglycerides, electrocardiogram (ECG), chest pain, shortness of breath, and cough. In making the system, 90 data were used which were then divided into two types of data, namely 70 training data and 20 testing data. The fuzzy inference system used in this study is the Mamdani inference system which uses centroid defuzzification and maximum defuzzifier mean of maxima (MOM). This defuzzification process is used to diagnose types of coronary artery disease, namely: CAD level 1 (Asymptomatic), CAD level 2 (Angina Pectoris), and CAD level 3 (Acute Myocardial Infarction). Results: The results of the research on the application of the fuzzy system for diagnosing coronary artery disease were the accuracy of the defuzzification centroid method of 98.5% for training data and 95% for testing data, while for the MOM defuzzification method, the accuracy level of training data was 98.5% and 90% for testing data. Conclusions: Based on the research results, it can be concluded that the centroid defuzzification method is better than the MOM defuzzification system for the coronary artery disease diagnosis system, so, it can be said that by using a fuzzy system with centroid defuzzification, the probability of correctly diagnosing one patient is 95%.

Proficiencies of different fuzzy inference systems in predicting the production performance of broiler chickens

Animal farming is a complex biological system because the responses of animal performance are nonlinear. In addition, thermal variables and management practices interact dynamically, making it difficult to ensure animal welfare and performance. As a result, modeling production performance under different thermal conditions is a complex task that requires predictive models. This study compared fuzzy models developed with different configurations using Mamdani and Sugeno inferences applied to the prediction of feed conversion in broilers. An experiment was conducted in four stages with a total of 240 Cobb 500 broiler chicks. The broilers were housed in climate-controlled wind tunnels and subjected to different temperatures (24, 27, 30, or 33 °C) and exposure times (1, 2, 3, or 4 days) inside cages equipped with feeders and drinkers. Feed intake and weight gain were quantified after 21 days. In both inference methods, the input variables (temperature and exposure time) were represented by both triangular and Gaussian functions. The output variable (feed conversion) was represented by singleton functions in the Sugeno inference system and by triangular and Gaussian functions in the Mamdani inference system. In addition to varying the types of membership functions in the representation of the data, all defuzzification methods of each methodology were also used. A comparison of the values predicted by each model and those obtained experimentally demonstrated that both the type of membership function and the defuzzification method influenced the final result of the prediction, with the triangular functions being better suited to the Sugeno system and the Gaussian functions being better suited to the Mamdani system for all defuzzification methods.

Reclamation of treated municipal wastewater in cooling towers of thermal power plants: Determination of the wastewater quality index

Water quality in cooling towers of thermal power plants (CTTPP) has a critical role in the operation and maintenance of CTTPP and the need for an index which represents well the quality of water used in CTTPP. In this study, municipal wastewater effluent (MWE) as a source of water for use in the CTTPP was evaluated by determining the water quality index. 22 MWE quality parameters were considered in terms of their potential effects on cooling towers. The relative weight of the parameters is calculated by the analytical hierarchical process (AHP). The analysis of water classification was done by using the fuzzy inference system (FIS). In the fuzzy logic approach, the trapezoidal and triangular membership functions and the Mamdani inference system were used, and the final rules consist of 240 rules. The center-of-gravity method is used for the Defuzzification of the results. The values obtained from the cooling tower effluent quality index (CTEQI) based on the FIS were generally more stable than the values from CTEQI based on the traditional system. The outcomes of this study established that the quality of effluent entering the cooling towers in a Shahid Moftteh power plant in the Hamadan (effluent sampling site) is moderate to good condition (annual fuzzy index 50–90, average annual traditional index 79.52–95.99). This research result reveals that CTEQI can be counted as a general tool for evaluating the quality of effluent that use in the CTTPP and can be used with confidence for this purpose.

Motivation assessment model for intelligent tutoring system based on mamdani inference system

<span lang="EN-US">Many educators have used the benefit offer by intelligent tutoring system. To become more personalizing and effective tutoring system, student characteristics need to be considered. One of important student characteristic is motivation. Therefore, in this study a motivation assessment model based on self-efficacy theory was proposed. Refer to the theory, effort, choice of activities, performance and persistence were discussed as motivation attributes. Further, time spend, difficulty level, number of correct answers and number of questions skipped are the parameters was defined for each attribute. The model was designed by taking the advantages of Mamdani inference system as fuzzy logic technique to predict students’ motivation level. The model able to inmates like a human tutor does in the traditional classroom to understand students’ motivation level.</span>

Flood-Prone Susceptibility Analysis In Garut Using Fuzzy Inference System Mamdani Method

Floods frequently occur in The District of Garut, representing a natural disaster. Currently, the National Disaster Management Agency (BNPB) is actively engaged in monitoring the landscape conditions of the Watershed River (DAS) to mitigate the impact of floods. To better understand and map this specific natural disaster, a program has been developed for in-depth analysis. This program has successfully assessed the susceptibility level to floods and categorized various areas in Garut as safe, prone, or flood-prone. To elaborate further, this study utilizes the Fuzzy Inference System Mamdani as a calculation process. Several factors influence this calculation process, including rainfall intensity, watershed area, land slope, altitude, population density, and risk level. Overall, the research findings indicate a 92.30% level of accuracy compared to the flood-prone catalog released by the national government, demonstrating its applicability in real-world scenarios.

Mamdani Fuzzy Expert System for Online Learning to Diagnose Infectious Diseases

E-learning and expert systems can be implemented for learning in the health sector. Through the e-learning system, prospective health workers can analyze problems by exploring the material in the system. However, material learning alone is less effective, so case study-based learning using an expert system is needed to strengthen understanding. The research applies an expert system to online learning to diagnose several infectious diseases. The disease diagnosis process uses the backward chaining method and the Mamdani fuzzy inference system. The fuzzy Mamdani inference system determines the intensity of disease severity so that appropriate treatment recommendations can be made. The test findings on 15 test datasets yielded a backward chaining accuracy value of 100%. Three test scenarios were used to establish the test using the Mamdani fuzzy inference method. Scenario 1: Testing with the Center of Gravity defuzzification and Fuzzy Mamdani Min inference system Tests employing the Fuzzy Mamdani Min inference method and center average defuzzification are used in Scenario 2. Scenario 3 involves testing using the Fuzzy Mamdani Product Inference System with Center Average Defuzzification. The average outcome for the intensity of disease severity utilizing the Fuzzy Mamdani Min inference system with Center of Gravity defuzzification was greater than that of the two test scenarios that were suggested, which was 49.43%.

Robotic Arm Position Control using Mamdani Fuzzy Logic on Arduino Microcontroller

A robotic arm is the most often used robot in manufacturing to perform the same task accurately again and over again in a controlled environment. However, direct positioning control of the robotic arm is always inaccurate as it does not consider its position in the external environment. Therefore, this work implements the Mamdani fuzzy logic in the position control of the 6 DOF robotic arm to improve the accuracy and movement of each joint at this manipulator. The implemented fuzzy logic Mamdani inference system is done in MATLAB and finally converted to the C language to accommodate the main microcontroller, Arduino UNO environment. There are 6 servo motors controlled by this approach and the result is compared with the conventional method. It is found that the Mamdani fuzzy logic controller has an average error of 0.67% while the direct control method has an average error of 2.33%. Based on this result, it shows that the use of the Mamdani fuzzy logic increases the accuracy and reduces the swaying movement after stopping the robotic arm movement.

Pricing strategies for online hotel searching: a fuzzy inference system procedure

PurposeHotels are considered one of the keys to tourism industry, without which it is impossible to visualize this industry. Setting the proper price for hotels has always been a nuisance for the decision makers because of its direct relationship with the demand for hotels. Thus, in the current study a Stackelberg game between the government (leader) and the hotels (follower) has been presented to determine the optimal price under competitive conditions. The selected hotels are different with respect to energy consumption and the environmental impact. Thus, the government makes efforts to control their prices with incentives and tariffs.Design/methodology/approachThe fuzzy inference system (FIS) has also been applied to forecast the hotel demand. Therefore, first off, the demand forecast criteria have been chosen by the experts and in the continuation, it has been screened by fuzzy Delphi approach. Finally, the quantity of hotel demand is computed by the Mamdani inference system. A mathematical model has been presented for determining the optimal sequencing of hotels and minimizing the searches to find a hotel.FindingsA case study based on the data extracted from online travel agencies (OTAs) has been presented to validate the proposed model. The results demonstrate that by the ranking position increase, the number of the tourists decreases and the higher the star number of a hotel, the lower its ranking position.Originality/valueConsidering the energy saving and environmental impacts in hotel pricing and considering the government's intervention in hotel revenues regarding the incentives and tariffs are the innovations of the present study.

Design of Three Control Algorithms for an Averaging Tank with Variable Filling

Abstract An averaging tank with variable filling is a nonlinear multidimensional system and can thus be considered a complex control system. General control objectives of such object include ensuring stability, zero steady-state error, and achieving simultaneously shortest possible settling time and minimal overshoot. The main purpose of this research work was the modeling and synthesis of three control systems for an averaging tank. In order to achieve the intended purpose, in the first step, a mathematical model of the control system was derived. The model was adapted to the form required to design two out of three planned control systems by linearization and reduction of its dimensions, resulting in two system variants. A multivariable proportional-integral-derivative (PID) control system for the averaging tank was developed using optimization for tuning PID controllers. State feedback and output feedback with an integral action control system for the considered control system was designed using a linear-quadratic regulator (LQR) and optimization of weights. A fuzzy control system was designed using the Mamdani inference system. The developed control systems were tested using theMATLAB environment. Finally, the simulation results for each control algorithm (and their variants) were compared and their performance was assessed, as well as the effects of optimization in the case of PID and integral control (IC) systems.

Soil Nutrient Detection and Recommendation Using IoT and Fuzzy Logic

Precision agriculture is a modern farming practice that involves the usage of Internet of Things (IoT) to provide an intelligent farm management system. One of the important aspects in agriculture is the analysis of soil nutrients and balancing these inputs are essential for proper crop growth. The crop productivity and the soil fertility can be improved with effective nutrient management and precise application of fertilizers. This can be done by identifying the deficient nutrients with the help of an IoT system. As traditional approach is time consuming, an IoT-enabled system is developed using the colorimetry principle which analyzes the amount of nutrients present in the soil and a fuzzy expert system is designed to recommend the quantity of fertilizers to be added in the soil. A set of 27 IF-THEN rules are framed using the Mamdani inference system by relating the input and output membership functions based on the linguistic variable for fertilizer recommendation. The experiments are conducted using MATLAB for different ranges of Nitrogen (N), Phosphorous (P) and Potassium (K). The NPK data retrieved by the system is sent to the ThingSpeak cloud and displayed on a mobile application that assists the farmers to know the nutrient information of their field. This system delivers the required nutrient information to farmers which results in efficient green farming.

A web-based fuzzy risk predictive-decision model of de novo stress urinary incontinence in women undergoing pelvic organ prolapse surgery.

Background:Pelvic organ prolapse (POP) and stress urinary incontinence (SUI) are common conditions affecting women's health and quality of life. In 50% of cases, SUI occurs after POP surgery, which is called de novo SUI. Predicting the risk of de novo SUI is a complex multi-attribute decision-making process. The current study made available a Decision Support System in the form of a fuzzy calculator web-based application to help surgeons predict the risk of de novo SUI.Materials and methods:We first identified 12 risk factors and the diagnostic criteria for de novo SUI by means of a systematic review of the literature. Then based upon an expert panel, all risk factors were prioritized. A set of 232 fuzzy rules for the prediction of de novo SUI was determined. A fuzzy expert system was developed using MATLAB software and Mamdani Inference System. The risk prediction model was then evaluated using retrospective data extracted from 30 randomly selected medical records of female patients over the age of 50 without symptoms of urinary incontinence who had undergone POP surgery. Finally, the proposed results of the predictive system were compared with the results of retrospective medical record data review.Results:The results of this online calculator show that the accuracy of this risk prediction model, at more than 90%, compared favorably to other SUI risk prediction models.Conclusions:A fuzzy logic-based clinical Decision Support System in the form of an online calculator for calculating SUI prognosis after POP surgery in women can be helpful in predicting de novo SUI.

Environment humidity and temperature prediction in agriculture using Mamdani inference systems

<span>This paper presents the results of a humidity and temperature prediction model in the environment for agriculture, using diffuse sets and optimizing their parameters by heuristic methods, such as genetic algorithms, and exact methods such as Quasi-Newton. It has been identified that non-specialized users could have difficulties in understanding the system dynamics and the behavior of variables over time. The aim of this research is obtain models with a high level of interpretability and accuracy that allows predicting the temperature and humidity values for the environment. The use of fuzzy logic to present a solution has great advantages as this system is highly rated for interpretability. Furthermore, by relating the obtained values for environment humidity and temperature to qualitative categories as high, medium or low, it allows non-specialized users to have a better understanding of the system dynamics. Two optimization techniques are applied to two different diffuse sets that allow the prediction of the humidity and temperature. It is found that the best implementation involves a Mamdani fuzzy inference system optimized with Quasi-Newton algorithm that uses a set of initial values attained through a previous optimization process with a genetic algorithm.</span>

Comparative Study between Type I and Type II Fuzzy Controller for Semi Active Suspension System

In this article, a type-2 fuzzy interval controller is proposed to solve the nonlinear control problems of semi-active suspension system. A suspension model with two degrees of freedom and A fuzzy approach for controller synthesis were proposed. The performance of the IT2FLC-based semi-active vehicle suspension system in terms of sprung mass displacement, suspension deflection and tire deflection are compared to the homologous fuzzy type-1 controller (T1FLC), and to the passive suspension system conventional using MATLAB / SIMULINK software for simulation and controller design. The vehicle parameters, called suspension deflection and speed of suspended mass are given as inputs for both controllers. The Csemi control signal is the variable damping coefficient. Inputs and outputs are presented by triangular membership functions. Mamdani inference system is used, along with a Karnik-Mendel algorithm to locate the center of gravity in reduction type for IT2FLC controller. Simulation results show that IT2FLC-based semi-active suspension system outperforms T1FLC and passive suspension system. Thus, they show a major improvement in control signal i.e. IT2FLC controller generates a lower damping coefficient than T1FLC controller. In addition, a remarkable reduction in signal energy by IT2FLC compared to same semi-active suspension system with T1FLC.