Mamdani Fuzzy Inference Research Articles

A Mamdani fuzzy inference system with trapezoidal membership functions for investigating fishery production

Seas, marine ecosystems, and coastal regions are crucial components of our environment. Numerous scientific strategies have been adopted to boost fisheries and aquaculture productivity. This study proposes a fuzzy-logic-based model to produce fisheries in India, which ranks fourth worldwide for fisheries production. Five input variables, such as fish seed, export, post-harvesting, released fund, and temperature, are considered inputs, and the production of fisheries is taken as the output variable. A Mamdani-type fuzzy inference system with trapezoidal membership functions is prepared with 243 rules in the IF-THEN format. This mathematical model investigates the impacts of input parameters on the production of Indian fisheries. We fit the model with the real-world data and show that fish seed, export, released fund, and post-harvesting facilities positively impact fisheries production. However, a very high temperature is unsuitable for high production, even if all other parameters lie at their desired level.

Intelligent Traffic Light Time Cycle Simulation Model using Fuzzy Mamdani

The growth of motorized vehicles in Indonesia has increased significantly. According to data from the Central Bureau of Statistics, the number of motorized vehicles in Indonesia has increased by around 10% each year in the last five years. One of the negative impacts of the increasing number of motorized vehicles is traffic congestion. Traffic congestion has become a serious problem in several cities in Indonesia. One of the causes is the increase in the number of vehicles at road intersections, which has an impact on congestion and the safety of road users. The rapid growth in the number of vehicles requires a more comprehensive strategy to reduce congestion and accidents at road intersections. Therefore, the need for Intelligent Transportation System, especially on the time-cycle configuration of intelligent red light is very important. This research aims to model the time-cycle of the red light using the Mamdani Fuzzy Inference System to simulate the green light time configuration so as to reduce the waiting time of road users at highway intersections. The simulation results show that the time-cycle configuration and green light time length of the Mamdani Fuzzy calculation are more varied relative to the number of vehicles. The values are relatively smaller than 6 to 54 seconds from the time configuration set by the local Department of Transportation. This shows a time efficiency for road users of up to 27%, which means that road users can complete trips 6 to 13 seconds faster.

Application of Mamdani Fuzzy Inference Method in Selection of Contract Employees of PT Kreasi Utama Mandiri

This study aims to improve the performance of the employee selection system. This research uses the Mamdani Fuzzy Inference System to determine the suitability of job applicants for the company. Recommendations for job applicants can be used as a reference for selecting permanent employees. The common problem in selecting permanent employees is the difficulty for the company in assessing and selecting competent job applicants, as the company has to compare the test results of each job applicant one by one, requiring a long time. In addition, the most difficult thing in making decisions is eliminating someone's subjectivity, so that every decision made is objective based on the criteria expected by the company. The results of this study proved that by applying mamdani fuzzy inference to the selection of contract employees at PT. Kreasi Utama Mandiri can facilitate management in selecting contract employees with an accuracy of 90% when compared to HRD assessments.

Mamdani-type fuzzy inference system for irrigational water quality

This paper aims to evaluate the quality of groundwater resources used for irrigation purposes using a Mamdani-type fuzzy inference system (MFIS). The MFIS is used to resolve ambiguities and uncertainties in economic, social, and natural systems and also facilitates the capture of expert knowledge in ways similar to human reasoning and thought processes. In this study, 20 groundwater samples were collected from various locations within the Mayiladuthurai district, Tamil Nadu, India, between January 2016 and December 2019. These samples underwent physical and chemical analyses to assess the suitability of the collected water resources for irrigation. The analysis utilizes the Mamdani Fuzzy Inference System, which combines values of Electrical Conductivity (EC) and Sodium Adsorption Ratio (SAR). Additionally, methods from the US Salinity Laboratory Staff were also employed. Ultimately, the groundwater quality in Mayiladuthurai is graded for irrigation use by this method. The results indicate that the MFIS reduces imprecision and uncertainty in data handling through the fuzzy membership function. The comparison of irrigation suitability results clearly demonstrates that the proposed MFIS method offers an improved assessment of the irrigation water quality level of the studied groundwater resources.

Optimizing Blood Glucose Regulation in Type 1 Diabetes Patients via Genetic Algorithm-Based Fuzzy Logic Controller Considering Substantial Meal Protocol

Effective management of blood glucose levels in individuals with type 1 diabetes, especially after meals, is crucial for diabetes care. Artificial pancreas systems (APS) perform automated insulin delivery in subjects with type 1 diabetes mellitus (T1DM). In this study, an optimized fuzzy logic controller was designed to achieve a euglycemic range after a substantial meal intake. All in silico simulations were performed using the MATLAB/Simulink environment, leveraging control variability grid analysis (CVGA), and the performance of the controller was evaluated. The proposed controller is based on a fuzzy-logic control law designed in three stages. First, a nonlinear framework of the glucose-insulin regulatory system was identified based on the heavy meal protocol of three patients given as follows: for subject ID 117-1, a total of 295 gCHO; for subject ID 126-1, 236 gCHO; and subject ID 128-1, 394 gCHO over a day. Then, an iterative tree structure was employed to establish a stabilizing control rule for insulin delivery, integrating inputs from two Mamdani Fuzzy Inference System (FIS) objects. Finally, a genetic algorithm refines the control system by fine-tuning the uncertainty of the fuzzy membership functions. Two scenarios were considered for three patients to assess the performance of the proposed controller. The results indicated its effectiveness under various conditions, achieving a time in the range of 61.25%, 71% and 61.10% respectively for the three subjects. The obtained results are analyzed and compared with IMC and multi-objective output feedback controllers. The findings of the study reveal that the proposed controller shows promising advancements in tailored strategies for type 1 diabetes patients, outperforming the other controllers in terms of blood glucose regulation.

Short-Term Electrical Energy Demand Forecasting Using Fuzzy Logic Technique

Minimization of electrical energy wastage and provision of its regular supply make electrical load forecasting an important aspect of the power infrastructure. An accurate load forecasting is crucial for the reduction of the cost of electrical energy generation and spinning reserve capacity. Therefore, in this study, fuzzy logic (FL) technique was employed for the projection of electrical energy demand on short-term basis. The FL model was trained using the six months hourly load and temperature data respectively obtained from 132/33 kV Ikeja West Transmission Station, Ayobo and Nigerian Meteorological Agency, Oshodi, Lagos State, Nigeria. Triangular and trapezoidal membership functions (MFs) were used in the training of the model with Mamdani fuzzy inference system. The time, load and temperature inputs were fuzzified into six, four and three MFs respectively while the fuzzification process used 25 fuzzy rule bases. The centroid method of defuzzification was used to change the results into readable values. The adequacy of the FL model was determined using five metrics including mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), chi square (χ2) and F- test. The obtained results revealed that the FL model developed excelled in all the five tests of adequacy considered with MSE, MAE, MAPE, χ2 and F-test values of 4.17, 6.74, 11.51%, 7.93 and 1.27 respectively and hence, performed satisfactorily. This work established that the fuzzy logic approach is appropriate for electrical load projection on short-term basis.

Rule Based Mamdani Fuzzy Inference System to Analyze Efficacy of COVID19 Vaccines

INTRODUCTION: COVID-19 was declared as most dangerous disease and even after maintaining so many preventive measures, vaccination is the only preventive option from SARS-CoV-2. Vaccination has controlled the risk and spreading of virus that causes COVID-19. Vaccines can help in preventing serious illness and death. Before recommendation of COVID-19 vaccines, clinical experiments are being conducted with thousands of grown person and children. In controlled situations like clinical trials, efficacy refers to how well a vaccination prevents symptomatic or asymptomatic illness.
 OBJECTIVES: The effectiveness of a vaccine relates to how effectively it works in the actual world.
 METHODS: This research presents a novel approach to model the efficacy of COVID’19 vaccines based on Mamdani Fuzzy system Modelling. The proposed fuzzy model aims to gauge the impact of epidemiological and clinical factors on which the efficacy of COVID’19 vaccines.
 RESULTS: In this study, 8 different aspects are considered, which are classified as efficiency evaluating factors. To prepare this model, data has been accumulated from various research papers, reliable news articles on vaccine response in multiple regions, published journals etc. A set of Fuzzy rules was inferred based on classified parameters. This fuzzy inference system is expected to be of great help in recommending the most appropriate vaccine on the basis of several parameters. 
 CONCLUSION: It aims to give an idea to pharmaceutical manufacturers on how they can improve vaccine efficacy and for the decision making that which one to be followed.

Fuzzy Logic Controllers Design for the Path Tracking of an Autonomous Coaxial Octorotor

Due to the essential characteristics and utility of coaxial octocopter vehicles in various real-time applications and many actual domains, this article aims to propose intelligent fuzzy logic controllers (FLC) for a six-degrees-of-freedom coaxial octorotor with a dynamic model. Using expert knowledge, the developed control structure is applied to track reference paths in the 3D environment with sufficient stability and effectiveness. The tested octorotor dynamic model is divided into six subsystems: altitude, pitch, roll, yaw, x position, and y position. Meanwhile, a Mamdani fuzzy inference controller is designed and applied for each subsystem to generate the appropriate control action. The simulation and the evolution tasks are done using a MATLAB dynamic model of the controlled process in order to perform and evaluate the proposed intelligent control structures of the 6 DOF octorotor model. The illustrated simulation results demonstrate the designed fuzzy controllers' effectiveness in motion stability and error tracking for different types of paths and all tested variables.

Intelligent decision support system for pulmonary tuberculosis detection using bipolar fuzzy utility matrix and bipolar Mamdani fuzzy inference system

Tuberculosis (TB) stands as the second leading global infectious cause of death, following closely behind the impact of COVID-19. The standard approach to diagnose TB involves skin tests, but these tests can yield inaccurate results due to limited access to healthcare and insufficient diagnostic resources. To enhance diagnostic accuracy, this study introduces a novel approach employing a Bipolar Fuzzy Utility Matrix Inference System (BFUMIS) and a Bipolar Mamdani Fuzzy Inference System (BMFIS) to assess TB disease levels. By considering factors associated with the causation of TB, the study devises suitable membership functions for bipolar fuzzy sets (BFS) using both triangular and trapezoidal fuzzy numbers. Using a point factor scale, the study clusters the rules systematically and assesses the level of uncertainty within these grouped rules by utilizing bipolar triangular fuzzy numbers (BTFN). To handle the BTFN, this study proposes converting bipolar triangular fuzzy into bipolar crisp score (CBTFBCS) algorithm as a defuzzification method. The optimal bipolar fuzzy utility sets (BFUS) are determined from the bipolar fuzzy utility matrix to identify patients’ TB disease levels. These sets play a pivotal role in characterizing the severity of TB disease levels in patients. Additionally, rigorous validation of the utility framework is accomplished through measures of bipolar fuzzy satisfactory factors and sensitivity analyses. Furthermore, the study introduces the BMFIS, which presents a novel perspective on the conventional fuzzy inference system. This innovative system integrates the Mamdani fuzzy inference system (MFIS) into a bipolar fuzzy context, enriching the diagnostic process with enhanced insights. To demonstrate the efficacy of the proposed methods, extensive validation is carried out using actual clinical data. The performance metrics used in this validation effectively demonstrate the superiority of the proposed approach.

An approach to diagnosis of prostate cancer using fuzzy logic

<p><span>Early diagnosis of cancers is a major requirement for patients and a complicated job for the oncologist. If it is diagnosed early, it could have made the patient more likely to live. For a few decades, fuzzy logic emerged as an emphatic technique in the identification of diseases like different types of cancers. The recognition of cancer diseases mostly operated with inexactness, inaccuracy, and vagueness. This paper aims to design the fuzzy expert system (FES) and its implementation for the detection of prostate cancer. Specifically, prostate-specific antigen (PSA), prostate volume (PV), age, and percentage free PSA (%FPSA) are used to determine prostate cancer risk (PCR), while PCR serves as an output parameter. Mamdani fuzzy inference method is used to calculate a range of PCR. The system provides a scale of risk of prostate cancer and clears the path for the oncologist to determine whether their patients need a biopsy. This system is fast as it requires minimum calculation and hence comparatively less time which reduces mortality and morbidity and is more reliable than other economic systems and can be frequently used by doctors.</span></p>

FUZZY CONTROL OF POWER CONSUMPTION OF ELECTROMECHANICAL EQUIPMENT OF THE MAIN DRAINAGE COMPLEX AT IRON ORE MINE

The paper presents a fuzzy control system for the consumption of electric power by pumps of the main drainage complex of a mine based on the Mamdani fuzzy inference algorithm. The proposed system has a structure with two inputs, such as water inflow and electricity cost, and one output, the power of pumping units. The choice of the Mamdani method for building a fuzzy system is due to the fact that all these inputs and outputs are subject to preliminary expert evaluation. All quantities are fuzzificated by smooth membership functions - Gaussian and sigmoid. The input values of the system are represented by linguistic variables, each of which includes five fuzzy sets that define the membership in the very low, low, medium, high, and very high levels of the corresponding input value. The output of the system contains ten fuzzy sets. During the fuzzification, it was assumed that in order to avoid overloading the pump motors, only one option is considered: their operation at maximum power when there is a very high level of mine water inflow during heavy rains or for other reasons. In all other cases, the operation of at least one of the pumps with the highest permissible power is excluded. A base of 25 fuzzy rules was formed. When defining the rules, it is assumed that in all cases when there is a high water inflow into the mine, all three pumps must be put into operation, regardless of the electricity cost in force in the current period of time. This set of rules is of the highest importance due to the need to prevent flooding of the mine. For other values of water inflow, the output power consumption of drainage pumping units depends on the current cost. This set of rules has a lower priority. We simulated the operation of the dewatering pumping system with and without a fuzzy power consumption control system. The test signal for the "water inflow" input was represented as a random variable with a normal distribution. The test signal for the "electricity cost" input was formed on the basis of real data on the price determined by the operator for the day ahead market for 12/24/2023. It was found that the use of a fuzzy system can reduce the cost of purchasing electricity by 1.24% per day. It should also be noted that the economic effect is achieved not by reducing the level of power consumption, but by rationalizing the operation of the drainage complex, which ensures maximum groundwater drainage performance when the cost of electricity is high, and minimum when it is low.

Security and Safety in Cyber-Physical System (CPS): An Inclusive Threat Model

A Cyber-Physical System (CPS) is a combination of computational algorithms and physical processes that are integrated together. Cyber-Physical Systems (CPS) integrate computer and communication capabilities with the monitoring and management of physical parts, establishing a mutually beneficial interaction between the cyber and physical components. Industrial Control System (ICS) is one example of CPS which integrates the physical (OT) and cyber domains (IT), which makes them more vulnerable to attacks. Two essential characteristics of Cyber-Physical Systems (CPS) are safety and security. Threat models are methods for identifying, analysing, and proposing security control countermeasures for threats and their capabilities. However, the threat model methods which are used for the traditional IT systems are not sufficient as they do not include the physical interactions, consequences and impacts to the safety aspects in the Operational Technology (OT). On the other hand, a risk assessment analyses attack scenario, examines cybersecurity from the attacker's point of view, and gives cost-benefit data to support the expenditure on security measures. This study proposes an inclusive attacker’s centric threat model and pro-active risk assessment model for CPS using Mamdani Fuzzy Inference System (FIS). The outcomes of the threat model prove that the lateral propagation of the threat is possible and threat may also propagate from the CPS assets to the IT segment. The risk assessment by using FIS shown that the safety and security risk for the CPS is significant and calculated as medium level. Hence, the risk factors that are considered in calculating the overall risk for a CPS need to be immediately addressed and mitigated.

A fuzzy inference system for predicting outbreaks in emerging infectious diseases

The world population is vulnerable to emerging and possibly re-emerging infectious diseases. Climate change, globalization, increased travelling, and urbanization are dynamic factors behind disease emergence and reemergence. This study formulates and analyzes a model with five inputs, viz. Change of Landscape (CoL), Gateway of Travel (GoT), Hygiene, Sanitization, Housing (HSH), Health Infrastructure (HI), and Regularity of Surveillance (RoS) and one output, viz. Possibility of Outbreak to determine the effect of these variables on disease emergence using a Mamdani fuzzy inference system. We study the impact of these factors on disease emergence and the possibility of an outbreak in a particular region.

Assessment of irrigational suitability of groundwater in Thanjavur district, Southern India using Mamdani fuzzy inference system

Groundwater is an important source for the agricultural needs in Thanjavur district, Tamil Nadu State, India. This study focused on assessing the groundwater suitableness for irrigation by employing Mamdani fuzzy inference system (MFIS). The groundwater quality data of premonsoon (PRM) and postmonsoon (POM) seasons were used for this purpose. Based on the mean value, the dominant cations were in the sequence of sodium > magnesium > calcium > potassium and the dominant anions order was bicarbonate > chloride > sulphate > nitrate. The Gibbs diagram showed that the major ionic concentration of groundwater was primarily influenced by rock-water interaction and the evaporation process. To determine groundwater suitability for irrigation purposes, sodium adsorption ratio, electrical conductivity, sodium percentage, residual sodium carbonate, magnesium ratio and Kelly's ratio were estimated. Then a Mamdani fuzzy inference system was developed with three input and one output variable. The fuzzy output categorized sample as “good” (PRM-36%, POM-44%) “good to permissible” (PRM-7%, POM-2%), “permissible” (PRM-24%, POM-23%), “permissible to poor” (PRM-5%, POM-4%) and “poor” (PRM-28%, POM-27%). On comparing the output of conventional irrigation water quality classification method, the developed model was found to be more accurate than the conventional method for classifying the irrigation water suitability. This model will assist decision-makers to develop strategies for the long-term management of groundwater resources in the study area.

Intelligens asztal kiszolgálása fuzzy alapú ütemezési módszer segítségével

This paper presents a fuzzy logic-based scheduling method, the Compact Mamdani Fuzzy Inference System (CMFIS), for improving decision-making in manufacturing. CMFIS allows quick evaluation of various scheduling scenarios and can replace shift managers by efficiently handling multiple objectives like prioritization, production capabilities, and time uncertainties. A case study at ESZO Ltd. illustrates CMFIS’s application in optimizing cutting operations. The system considers machine capacities and material dimensions, dynamically adjusting machine assignments for efficient production. Configured through an Excel-based framework, it processes inputs and produces clear outputs through defuzzification, offering a practical tool for enhancing manufacturing decision-making.

Multicriteria fuzzy statistical analysis of biogas energy systems dependability

The work is devoted to the problems of assessing the dependability of autonomous energy systems with biogas type of electrical generation. The purpose of the work is to develop an intelligent tool for multi-criteria dependability assessment, taking into account the statistical uncertainty of individual indicators. A three-level hierarchy (according to the degree of generalization) of dependability indicators, represented by statistical (at the lower level) and fuzzy linguistic (starting from the second level) variables, has been developed. It is proposed to implement the transition from statistical values of lower-level indicators to numerical values of second-level indicators using an artificial neural network. Fuzzification of second-level indicators was carried out using L. Zadeh’s z-number apparatus, which allows taking into account statistical uncertainty. To determine the integral dependability indicator (top of the hierarchy) based on second-level indicators, it is proposed to use the Mamdani fuzzy inference algorithm. The constructed procedure for determining the level of dependability allows us to obtain data for making scientifically based decisions when operating biogas energy systems.

Decision Support System for Single Tuition Scholarship Awardees in Higher Education Using Mamdani Fuzzy Inference

Universitas Madura implements the Single Tuition Fee (UKT) scholarship program in the Informatics department. The current UKT scholarship selection system uses a traditional model that is still not effective, causing obstacles such as inflexibility in registration time. The print-out documents are vulnerable to damage or loss and difficulty searching when it required. The criteria in the current system consisted of a minimum Grade Point Average (GPA) of 3, a letter of family condition, and also student status in semesters 3 to 7. The three criteria are not enough to determine a scholarship candidate. The recruitment process involves only the Head of the Study Program (Kaprodi). The Informatics study program still has many candidates applying for scholarships reaching around 280%. This research proposes a Decision Support System (DSS) using Fuzzy Mamdani with six criteria, including GPA, Achievement, Parents' Income, Parents' Dependents, Semester, and History of not receiving scholarships with the aim of overcoming these problems. The results show that the performance of the proposed SPK is very good, it is shown by the MAPE value of less than 10% and more efficient time than the current system. This system has also been in accordance with the required functions through the black test.

Reckoning the performance of management institutions – A Mamdani fuzzy inference system approach

PurposeThe purpose of this research is to present an integrated methodological framework to aid in performance stewardship of management institutions according to their strategies based on a holistic evaluation encompassing social, economic and environmental dimensions.Design/methodology/approachA Mamdani fuzzy inference system (FIS) approach was adopted to design the quantitative models with respect to balanced scorecard (BSC) perspectives to demonstrate dynamic capability. Individual models were developed for each perspective of BSC using Mamdani FIS. Data was collected from subject matter experts in management education.FindingsThe proposed methodology is able to successfully compute the scores for each perspective. Effective placement, teaching learning process, faculty development and systematic feedback from the stakeholders were found to be the key drivers for revenue generation. The model is validated as the results were well accepted by the head of the institution after implementation.Research limitations/implicationsThe model resulting from this study will assist the institution to cyclically assess its performance, thus enabling continuous improvement. The strategy map provides the causality of the objectives across the four perspectives to aid the practitioners to better strategize. Also this study contributes to the literature of BSC as well to the applications of multi-criteria decision-making (MCDM) techniques.Originality/valueMamdani FIS integrated BSC model is a significant contribution to the academia of management education to quantitatively compute the performance of institutions. This quantified model reduces the ambiguity for practitioners to decide the performance levels for each metric and the priorities of metrics.

Analysis of Users’ Requirements for Public Waste Management Services Using Fuzzy Inference

Municipalities play a key role in public waste management ensuring effective and efficient service performance. In Colombia, the public utilities sector has undergone significant changes since decentralization and the entry of private companies into the sector. In this study, our purpose is to analyze user perceptions and their willingness to pay for additional services regarding waste management. By using data analysis methods and a Mamdani fuzzy inference system, we were able to identify users’ service requirements and expected quality. According to the results of our analysis, a combination of minimum coverage and low frequency resulted in a tariff increase of 7.05%. Furthermore, we recommend expanding the model to include other waste management services, such as solid waste collection, as well as to consider environmental aspects and sustainable practices.

Modified Mamdani-fuzzy inference system for predicting the cost overrun of construction projects

Cost overruns are a common worldwide problem in the construction industry; improved proactive risk management and cost control are much needed. Several models have been proposed, but all have weaknesses, particularly in data demands and the severity of critical risks or uncertainties associated with expert judgment. In response, this study develops a new 3-part model based on the Mamdani-type fuzzy inference system (FIS) to predict the cost overrun of construction projects. The first part assesses the weight of each expert, evaluating the severity of cost overrun factors. The second part contains a list of 40 in-built cost overrun factors and their degree of severity, while the third part establishes the relationships of every factor’s occurrence probability and severity to predict the cost overrun of a specific project. The severity of each factor is assessed based on a survey of 31 randomly selected experts in the Saudi Arabian construction industry. The model is demonstrated on two completed projects in Saudi Arabia. For each project, this involves a group of project-based experts rating the probability of occurrence of each factor on that project and applying this to the factor severity list to obtain a predicted cost overrun (PCO) for the whole project. The model is validated for robustness by sensitivity analysis comparing the predicted and actual whole project cost overrun and shown to be of practical value in assessing critical risks and predicting the likely amount of cost overrun. The model is equally applicable in the early project stages.