Trapezoidal Membership Functions Research Articles

Automatic Finding Trapezoidal Membership Functions in Mining Fuzzy Association Rules Based on Learning Automata

Automatic Finding Trapezoidal Membership Functions in Mining Fuzzy Association Rules Based on Learning Automata

Prediction of weldment mechanical properties in GMAW with robot-assisted using fuzzy logic systems

Welding operation decides the quality of product standards in all metal work products like automobiles, aerospace vehicles, and many more. The quality of the welding process is more reliable by automating the process with robots. In this research work, the GMAW operation is automated with the ‘Fanuc Robot Arc mate 100iC/12’ robot. The material characteristics such as ultimate tensile strength, hardness, and impact strength of weldments are predicted using a fuzzy system using triangular membership function (TrMF) and trapezoidal membership function (TMF). The simulated results are validated by comparing with experimental work, the experiments are designed using orthogonal array L18, and material characteristics are studied using fractography test. The fuzzy system is trained with experimental results using the IF-Then rule base with the help of the L18 orthogonal array. The inference system has predicted the accuracy rate of weldment mechanical properties, showing a lower error rate.

SAFEA application design on determining the optimal order quantity of chicken eggs based on fuzzy logic

<span>The availability of stock in the chicken egg supply chain is influenced by the ability of egg Agents to determine the optimal orders to suppliers. The optimal number of orders is very important to manage for the Bogor City Egg Agent Indonesia because the stock capacity reaches 340 crates. The optimal number of orders for eggs at the Egg Agent is influenced by input variables, namely final stock (crate), selling price (crate), and consumer demand (crate) so that the inventory is under control. The three input variables have fuzzy values that must be processed using fuzzy logic to get the optimal number of orders to suppliers so that the egg stock in the warehouse is well maintained. The optimal order model for eggs in the smart application for egg agent (SAFEA) was developed using a fuzzy logic approach with the triangular and trapezoidal membership function. Based on the optimal order model in the SAFEA application, the optimal order to the supplier is 100-104 crates per day.</span>

Enhanced IoT-Based Climate Control for Oyster Mushroom Cultivation Using Fuzzy Logic Approach and NodeMCU Microcontroller

Urban farming has the potential to utilise unused space in the community to alleviate food shortages and increase the community’s income through local food production. When Internet of Things (IoT) technology is integrated with urban farming, it can further improve its efficiencies and yield. The work in this paper improved our previous work of using an IoT-based climate control system to regulate the cultivation environment of oyster mushrooms automatically. Even though the climate control system could produce two batches of mushroom yields, there were several limitations, such as less efficient climate control due to threshold-based corrective action, water wastage, and system instability. This paper aims to address these stated limitations by implementing a fuzzy logic algorithm and redesigned the climate control system. Two crisp input variables from DHT22 sensors representing temperature and humidity were fed into the Node MCU microcontroller’s fuzzy logic coded in C language. The temperature and humidity conditions were divided into five fuzzy trapezoidal membership functions resulting in 25 fuzzy rules to control the duration of running the water pump and ventilation fan. An internal, lightweight web server were managed all HTTP client requests. The enhanced system also included a safety measurement to avoid overheating the microcontroller and causing water wastage. Upon analysis of the data captured in two months, the result showed a decrease of 40% in water utilisation and an increase of mushrooms yield up to 226%. The enhanced climate control system also facilitated maintaining and controlling the temperature and humidity conducive for optimal mushroom cultivation.

A Novel Approach to Generate Type-1 Fuzzy Triangular and Trapezoidal Membership Functions to Improve the Classification Accuracy

Fuzzy logic is an approach that reflects human thinking and decision making by handling uncertainty and vagueness using fuzzy membership functions. When a human is engaged in the design of a fuzzy system, symmetric properties are naturally preferred. Fuzzy c-means clustering is a clustering algorithm that can cluster datasets to produce membership matrix and cluster centers, which results in generating type-1 fuzzy membership functions. However, fuzzy c-means algorithm has a limitation of producing only a single membership function type, Gaussian MF. Generation of multiple fuzzy membership functions is of immense importance as it provides more efficient and optimal solutions to a problem. Therefore, an approach to generate multiple type-1 fuzzy membership functions through fuzzy c-means is required for the optimal and improved results of classification datasets. Hence, to overcome the limitation of the fuzzy c-means algorithm, an approach for the generation of type-1 fuzzy triangular and trapezoidal membership function through fuzzy c-means is considered in this study. The approach is used to calculate and enhance the accuracy of classification datasets called iris, banknote authentication, blood transfusion, and Haberman’s survival. The proposed approach of generating MFs using FCM produce asymmetric MFs, whose results are compared with the MFs produced from grid partitioning (GP), which are symmetric MFs. The results show that the proposed approach of generating type-1 fuzzy membership function through fuzzy c-means is effective and can be adopted.

Examining child obesity risk level using fuzzy inference system

The doctor will determine the risk level of childhood obesity by using standard calculations, namely measuring the child's weight and height, and many other factors. Then the doctor will calculate the child's body mass index (BMI). The results of calculations made by the doctor will be compared with standard/normal values set by FAO/WHO, to obtain the level of risk of obesity in children. This study aims to analyze the risk level of obesity in children using the Sugeno method of Fuzzy Inference system, using the trapezoidal membership function and involving six input variables such as exercise habits, consumption of fast food, history of obesity of parents, and others. The application of the fuzzy inference system Sugeno method can help doctors to analyze the risk level of childhood obesity quickly and accurately with an accuracy rate of 85%. The results of the implementation of the Sugeno method of Fuzzy Inference system showed that out of 140 children who were the object of the study, 119 children received a diagnosis of the level of risk of obesity which was the same as the diagnosis made by a doctor.

A Takagi Sugeno based reactor power control of VVER-1000 using linear parameter varying identification of two-point kinetic model

This study aims to design Takagi Sugeno (TS) Fuzzy logic based power control for VVER-1000 PWR to achieve smooth and precise load following situations under various disturbances and model uncertainties. To achieve the purpose, a two-point kinetic model with consideration of neutron diffusion, thermal hydraulics and poison concentration is developed. Model identification for the equivalent linear parameter varying (LPV) system is performed using the fixed functional parameters and reproducing the corresponding LPV subsystems for each of those functional points in the form of SISO transfer function synthesis. Linear heft factors are introduced to compute the weighted output based on the triangular and trapezoidal membership functions of fuzzy logic. Linear interpolation is used to find the controlled power based on heft factors and operational specifications of the nuclear reactor over the entire power range. A TS fuzzy based controller with fixed gain and parameters is designed to efficiently control the reactor power under multiple transient conditions. The LPV subsystems are to be controlled with the fuzzy controller comprising two inputs with effective utilization of unsymmetrical, nonlinear and optimized membership functions and scaled output linear function of fuzzy variables. Stability analysis is performed using the Lyapunov like function and the condition is demonstrated for the two-point kinetic model of the reactor. The performance of the proposed framework is evaluated for load following scenarios with fast and slow ramp changes; along with accident handling with positive reactivity insertion tests. The proposed TS fuzzy power control model found preferably efficient in load tracking applications and disturbance rejection conditions.

Bead geometry modeling on uneven base metal surface by fuzzy systems for multi-pass welding

This paper presents a modeling method of weld bead profiles deposited on uneven base metal surfaces and its application in multi-pass welding. The robotized multi-pass tungsten inert gas welding requires precise positioning of the weld beads to avoid welding defects and achieve the desirable welding join since the weld bead shapes depend on the surface of the previously deposited beads. The proposed model consists of fuzzy systems to estimate the coefficients of the profile function. The characteristic points of the trapezoidal membership functions in the rule bases are tuned by the Bacterial Memetic Algorithm during supervised training. The fuzzy systems are structured as multiple-input-single-output systems, where the inputs are the welding process variables and the coefficients of the shape functions of the segments underlying the modeled bead; the outputs are the coefficients of the bead shape function. Each segment surface is approximated by a second-order polynomial function defined in the weld bead’s local coordinate system. The model is developed from empirical data collected from single and multi-pass welding. The performance of the proposed model is compared with a multiple linear regression model. During the experimental validation, first, the individual beads are evaluated by comparing the estimated coefficients of the profile function and other bead characteristics (bead area, width, contact angles, and position of the toe points) with the measurements, and the estimations of a multiple linear regression model. Second, the sequential placement of the weld beads is evaluated while filling a straight V-groove by comparing the estimated bead characteristics with the measurements and calculating the accumulated error of the filled groove cross-section. The results show that the proposed model provides a good estimation of the bead shapes during deposition on uneven base metal surfaces and outperforms the regression model with low error in both validation cases. Furthermore, it is experimentally validated that the derived bead characteristics provide a suitable measure to identify locations sensitive to welding defects.

Neuro-Fuzzy Transformation with Minimize Entropy Principle to Create New Features for Particulate Matter Prediction

Air pollution is a major global issue. In Thailand, this issue continues to increase every year, similar to other countries, especially during the dry season in the northern region. In this period, particulate matter with aerodynamic diameters smaller than 10 and 2.5 micrometers, known as PM10 and PM2.5, are important pollutants, most of which exceed the national standard levels, the so-called Thailand air quality index (T-AQI). Therefore, this study created a prediction model to classify T-AQI calculated from both types of PM. The neuro-fuzzy model with a minimum entropy principle model is proposed to transform the original data into new informative features. The processes in this model are able to discover appropriate separation points of the trapezoidal membership function by applying the minimum entropy principle. The membership value of the fuzzy section is then passed to the neural section to create a new data feature, the PM level, for each hour of the day. Finally, as an analytical process to obtain new knowledge, predictive models are created using new data features for better classification results. Various experiments were utilized to find an appropriate structure with high prediction accuracy. The results of the proposed model were favorable for predicting both types of PM up to three hours in advance. The proposed model can help people who are planning short-term outdoor activities.

Optimal design of type-2 fuzzy systems for diabetes classification based on genetic algorithms

Diabetes has become a global health problem, where a proper diagnosis is vital for the life quality of patients. In this article, a genetic algorithm is put forward for designing type-2 fuzzy inference systems to perform Diabetes Classification. We aim at finding parameter values of Type-2 Trapezoidal membership functions and the type of model (Mamdani or Sugeno) with this optimization. To verify the effectiveness of the proposed approach, the PIMA Indian Diabetes dataset is used, and results are compared with type-1 fuzzy systems. Five attributes are used considered as the inputs of the fuzzy inference systems to obtain a Diabetes diagnosis. The instances are divided into design and testing sets, where the design set allows the genetic algorithm to minimize the error of classification, and finally, the real behavior of the fuzzy inference system is validated with the testing set.

Proposed Ranking Function to Solve the Fuzzy Project Management and Network Problem

In this paper, we create a relationship between network model and project management by using data of cost to the residential project. we solved these data by using the crisp network method(real data). After that, by using the same data we convert it to fuzzy data then the fuzzy data tested by trapezoidal membership function to be sure its belong to the duration [0,1], Proposed ranking function had solved the fuzzy network problem to the fuzzy data.

GIS‐based groundwater quality assessment using GQIs and fuzzy‐logic approach

AbstractThis study proposes the evaluation of GIS‐based groundwater quality index (GQI) and spatial variability with a hybrid system that combines Fuzzy Logic with GIS‐based GQI. A case study in the semi‐arid area of Punjab, namely Rupnagar, illustrates the proposed hybrid outline. Two GIS‐based GQI models GQI‐10 (using ten water quality parameters (WQPs)) and GQI‐6 (using six “apprehensive/crucial” WQPs), as well as hybrid Fuzzy Logic GIS‐based GQI (FGQI) (using six crucial WQPs) models, were developed using trapezoidal membership functions and rule sets based on expert's knowledge. Performance comparison of GQI models showed that the FGQI model predicts improved groundwater quality than the GQI‐10 and GQI‐6. FGQI model variance, RMSE, and R2 values are 84.25, 6.49, 0.89, respectively. Results have shown that the developed FGQI model is successful and GQI modelling suggests the use of northern and central groundwater without treatment to be unsafe for drinking purposes.

Comparative Modelling of Strength Properties of Hydrated-Lime Activated RiceHusk-Ash (HARHA) Modified Soft Soil for Pavement Construction Purposes by Artificial Neural Network (ANN) and Fuzzy Logic (FL)

Artificial neural network and fuzzy logic based model soft-computing techniques were adapted in the research study for the evaluation of the expansive clay soil-HARHA mixture’s consistency limit, compressibility and mechanical strength properties. The problematic clay soil was stabilized with varying proportions of HARHA (stabilizing agent) which is an agricultural waste derivative from the milling of rice ranging from 0% to 12%; the utilization of the alkaline activated wastes encourages its recycling and re-use to obtain sustainable, eco-efficient and eco-friendly engineered infrastructure for use in the construction industry with economic benefits also. The obtained laboratory and experimental responses were taken as the system database for the ANN and fuzzy logic model development; the soil-HARHA proportions with their corresponding compaction and consistency limit characteristics were feed to the network as the model input variables while the mechanical strength (California-bearing-ratio (CBR), unconfined-compressive-strength (UCS) and Resistance value (R-values)) responses of the blended soil mixture were the model target variables. For the ANN model, feed forward back propagation and Levernberg Marquardt training algorithm were utilized for the model development with the optimized network architecture of 8-6-3 derived based on MSE performance criteria; while for the fuzzy logic model, the mamdani FIS with both triangular and trapezoidal membership function with both models formulated, simulated and computed using MATLAB toolbox. The models were compared in terms of accuracy of prediction using MAE, RMSE and coefficient of determination and from the computed results, 0.2750, 0.4154 and 0.9983 respectively for ANN model while 0.3737, 0.6654 and 0.9894 respectively was obtained for fuzzy logic model. The two models displayed robust characteristics and performed satisfactorily enabling the optimization of the solid waste derivatives utilization for soil mechanical properties improvement for engineering purposes.

Comprehensive Evaluation of Cloud Manufacturing Service Based on Fuzzy Theory

Comprehensive evaluation is the key to ensuring that cloud manufacturing services can run smoothly over the whole life cycle of products. It is therefore of great importance to carry out a careful scientific evaluation of cloud manufacturing services. In this paper, a combination of qualitative and quantitative methods was adopted based on fuzzy comprehensive evaluation (FCE), and a comprehensive evaluation index system and fuzzy trapezoidal membership function were established. At the same time, using the analytic hierarchy process (AHP) and entropy method, combination weights were obtained, and finally, a comprehensive evaluation model was determined using the five grades of “excellent”, “good”, “medium”, “qualified” and “poor”. Using 30 examples, it was verified that the established evaluation model could classify cloud manufacturing services effectively and systematically according to the different needs of users, thus providing a more effective reference for cloud manufacturing services.

Performance Evaluation of Different Membership Function in Fuzzy Logic Based Short-Term Load Forecasting

A mismatch between utility-scale electricity generation and demand often results in resources and energy wastage that needed to be minimized. Therefore, the utility company needs to be able to accurately forecast load demand as a guide for the planned generation. Short-term load forecast assists the utility company in projecting the future energy demand. The predicted load demand is used to plan ahead for the power to be generated, transmitted, and distributed and which is crucial to power system reliability and economics. Recently, various methods from statistical, artificial intelligence, and hybrid methods have been widely used for load forecasts with each having their merits and drawbacks. This paper investigates the application of the fuzzy logic technique for short-term load forecast of a day ahead load. The developed fuzzy logic model used time, temperature, and historical load data to forecast 24 hours load demand. The fuzzy models were based on both the trapezoidal and triangular membership function (MF) to investigate their accuracy and effectiveness for the load forecast. The obtained low Mean Absolute Percentage Error (MAPE), Mean Forecast Error (MFE), and Mean Absolute Deviation (MAD) values from the forecasted load results showed that both models are suitable for short-term load forecasting, however the trapezoidal MF showed better performance than the triangular MF.

Modeling of removal of an organophosphorus pesticide from aqueous solution by amagnetic metal–organic framework composite

Today, a variety of pesticides are used to fight plant pests in the world. The entry of these resistant pollutants into water resources can have devastating effects on human health and the environment, hence their removal from the environment is a vital task. In the present work, the magnetic iron-based metal–organic framework (Fe 3 O 4 /MIL-101 (Fe)) was synthesized by a simple and feasible method and characterized by FT-IR, XRD, BET, FESEM, TEM, TGA, and VSM techniques. The synthesized nanocomposite was successfully applied for the removal of fenitrothion (FEN) pesticide from the aqueous solutions. The isothermal and kinetic models were also investigated. The Langmuir isotherm model (type I) and pseudo-second-order kinetic model were more consistent in the adsorption process. The thermodynamic parameters of fenitrothion sorption were also calculated. The results revealed that the adsorption of fenitrothion onto Fe 3 O 4 /MIL-101 (Fe) was spontaneous and endothermic under optimized conditions. Moreover, the removal efficiency of FEN was predicted using the developed fuzzy logic model. Four input variables including the initial concentration of FEN (mg·L −1 ), pH of the solution, adsorbent dosage (mg), and contact time (min) versus removal efficiency as output were fuzzified by the usage of an artificial intelligence-based method. The fuzzy subsets consisted of Triangular and Trapezoidal membership functions (MFs) with six levels and a total of 23 rules in IF-THEN format which was applied on a Mamdani inference system. The obtained coefficient of determination value ( R pred 2 =0.98205) proved the excellent accuracy of the fuzzy logic model as a powerful tool for the prediction of FEN removal efficiency.

Application of Elman and Cascade neural network (ENN and CNN) in comparison with adaptive neuro fuzzy inference system (ANFIS) to predict key fuel properties of ABE-diesel blends

ABSTRACT Today, a growing interest to use Acetone-Butanol-Ethanol (ABE) as a biofuel has emerged. Fuel properties play important roles to determine engine’s performance, combustion, and emission behaviors. Yet, the determination of fuel properties is expensive and time-consuming. Previous studies on ABE did not provide information on how to predict its fuel properties. This study developed an Elman and Cascade neural networks (ENN and CNN) and compared their results with adaptive neuro inference system (ANFIS) to predict ABE’s key fuel properties. Three properties, i.e., calorific value, density, and kinematic viscosity were used as the target outputs, while ABE, acetone, butanol, and ethanol ratio were selected as the input parameters. The ENN and CNN models were trained using 10 different training algorithms, while the ANFIS model was examined using eight unique membership functions. To evaluate the prediction accuracy of each model, six different parameters were employed. Results showed that, compared to ENN and CNN, the ANFIS model gave the best performance accuracy with the least errors to predict the key fuel properties of ABE-diesel blends. For calorific value, density, and kinematic viscosity prediction, the best results of the ANFIS model were given by triangular, Pi curve, and trapezoidal membership functions, respectively. Therefore, ANFIS gave the best model of all the investigated models in this study.

INVESTIGATING THE POTENTIAL CONSEQUENCES OF THE MEMBERSHIP FUNCTIONS IN A FUZZY LOGIC CONTROLLER-BASED OBSTACLE CLIMBING ROBOT

Autonomous mobile robots are the robots which can perform desired tasks in unstructured environment without continuous human guidance. This paper deals with the design of an Autonomous obstacle climbing robot that will able to climb an obstacle. This obstacle climbing navigation system is utilized by the intelligent fuzzy logic controller. The controller has 2 inputs and single output system. The inputs are slope and terrain type and the output is the speed of the robot. The membership functions are the building blocks of fuzzy logic system and play a vital role in the performance of mobile robot. This paper describes the effect of different membership functions namely triangular, trapezoidal membership functions are considered.

Finding Suitable Membership Functions for Mining Fuzzy Association Rules in Web Data Using Learning Automata

Transactions in web data are huge amounts of data, often consisting of fuzzy and quantitative values. Mining fuzzy association rules can help discover interesting relationships between web data. The quality of these rules depends on membership functions, and thus, it is essential to find the suitable number and position of membership functions. The time spent by users on each web page, which shows their level of interest in those web pages, can be considered as a trapezoidal membership function (TMF). In this paper, the optimization problem was finding the appropriate number and position of TMFs for each web page. To solve this optimization problem, a learning automata-based algorithm was proposed to optimize the number and position of TMFs (LA-ONPTMF). Experiments conducted on two real datasets confirmed that the proposed algorithm enhances the efficiency of mining fuzzy association rules by extracting the optimized TMFs.

Design and analysis of inverse fuzzysoft set for triangular and trapezoidal membership function in decision making

Decision making plays a vital role within uncertain, imprecise situations in recent years. A numerical actualize called soft set by Molodtsov was started to bargain uncertain condition. Herein article, inverse triangular and trapezoidal fuzzy soft set theory is analyzed in decision making. Inverse fuzzysoft set theory is an extension of fuzzy soft set theory which gives deeper insights in decision making and gives a technique to portray the character of each object in the universe. An application of inverse triangular and trapezoidal fuzzy soft set is established in the course of case study in multi-criteria decision making.