Input Membership Functions Research Articles

Double yolk eggs detection using fuzzy logic.

Chicken egg products increased by 60% worldwide resulting in the farmers or traders egg industry. The double yolk (DY) eggs are priced higher than single yolk (SY) eggs around 35% at the same size. Although, separating DY from SY will increase more revenue but it has to be replaced at the higher cost from skilled labor for sorting. Normally, the separation of double yolk eggs required the expertise person by weigh and shape of egg but it is still high error. The purpose of this research is to detect double-yolked (DY) chicken eggs with weight and ratio of the egg’s size using fuzzy logic and developing a low cost prototype to reduce the cost of separation. The K-means clustering is used for separating DY and SY, firstly. However, the error from this technique is still high as 15.05% because of its hard clustering. Therefore, the intersection zone scattering from using the weight and ratio of the egg’s size to input of DY and SY is taken into consider with fuzzy logic algorithm, to improve the error. The results of errors from fuzzy logic are depended with input membership functions (MF). This research selects triangular MF of weight as low = 65 g, medium = 75 g and high = 85 g, while ratio of the egg is triangular MF as low = 1.30, medium = 1.40 and high = 1.50. This algorithm is not provide the minimum total error but it gives the low error to detect a double yolk while the real egg is SY as 1.43% of total eggs. This algorithm is applied to develop a double yolk egg detection prototype with Mbed platform by a load cell and OpenMV CAM, to measure the weight and ratio of the egg respectively.

Rainfall prediction by using ANFIS times series technique in South Tangerang, Indonesia

Excessive rainfall is one of the triggers for the flooding phenomenon, especially in the tropics with flat or concave areas. Some critical points in the South Tangerang region, which are currently one of the most rapidly developing cities, cannot be ignored from the flooding problem. Floods cause disturbing human activities, loss of life and property, and in turn affect the economic stretch in an area. This paper aimed to predict rainfall by exploring the application of artificial intelligence techniques such as ANFIS (Adaptive NeuroFuzzy Inference System). The proposed technique combines neural network learning abilities with transparent linguistic representations of fuzzy systems. The ANFIS model with various input structures and membership functions was built, trained, and tested to evaluate the capability of a model. Analyses of six-year rainfall data on a monthly basis in South Tangerang City, Banten found that rainfall prediction based on ANFIS time series is promising where 80% of data testing is well predicted.

Использование геометрической прогрессии для определения параметров функций принадлежности при настройке нечеткого регулятора ПИ-типа

In the issue we consider the possibility of geometric series application for fuzzy logic controller tuning. Triangular and trapezoidal membership functions are often used for fuzzy controller parameters description, and determination of those functions bases is a common way to tune the controller. Our related publications review revealed that there is no universal fuzzy controllers tuning method, and that approaches to the problem are usually quite difficult and not sufficiently described to understand and reiterate. Aim. So for the purpose of our research we assumed that bases of fuzzy controller input and output parameters membership functions could be described with a numerical sequence. In that case the entire tuning procedure may be reduced to mere selection of such numerical sequence parameters. Materials and methods. Geometric series with unit as a first member were chosen to avoid ambiguity, different ratios from 1.5 to 3.5 with an increment of 0.2 were chosen for second and third series members calculations. Calculated values were used as membership functions term bases for eleven subsidiary linguistic variables. Then we obtained ranges of control, control error and its time integral values variations for a specific control object using automatically tuned PI-controller. Using MATLAB FIS-editor we constructed PI-type fuzzy controller assuming that both of its input and an output parameters has five terms. By projecting the subsidiary linguistic variables gained earlier on each parameter range we obtain eleven different types of PI-type fuzzy controller tunings. They then were applied for time delay technological object controlling, seven of which proved to be functioning. Results. Comparison of functioning fuzzy controllers’ transient processes with corresponding ordinary PI-controller transient process allowed us to determine that all seven PI-type fuzzy controllers’ quality criteria values are much better that those of ordinary PI-controller. Conclusion. Our research showed that usage of geometric series can be an effective and easy way to obtain fuzzy controllers tunings of satisfactory quality.

An Integrated Framework for Optimal Irrigation Planning Under Uncertainty: Application of Soil, Water, Atmosphere and Plant Modeling

In this paper, an innovative framework is developed for simulating the water distribution in agricultural lands considering existing constraints related to soil, water, atmosphere and plant. Some nonlinear operating rules are formulated for the irrigation planning and groundwater management in Shahrekord plain in Iran. Evapotranspiration values are estimated based on a real-time modeling. Groundwater exploitations are limited for each irrigated area by considering its actual water requirement and soil moisture balance with daily time steps at the root zone. Moreover, this work introduces an approach for taking into account the uncertainty of available water. For this purpose, the membership functions of fuzzy inputs are discretized into five levels and then a multiobjective optimization model is developed to find the extreme values of economic efficiency of irrigation water for different levels. The results show that under limited water conditions, the economic productivity could be further improved when water, soil, atmosphere and crop relationships are simultaneously considered. In the proposed cropping pattern, the net annual return was increased by more than 43% comparing to the existing cropping pattern. Furthermore, different efficiency criteria for crops with higher values of yield production (e.g., potato, maize, sugar beet and alfalfa) are more affected by the existing uncertainties.

Fault Detection of Electromechanical Actuators via Automatic Generation of a Fuzzy Index

Regardless of dimensions, complexity, or purpose of a mechatronic system, fault detection (FD) represents a critical element to achieve robustness and safety, while reducing risks and maintenance costs. Important requisites for an FD component include flexibility, performance, platform independence, and interpretability. To pursue these goals, this article presents a framework for the FD on electromechanical systems based on a fuzzy inference system. Fuzzy logic (FL) is a powerful modeling tool whose applicability to complex systems, however, can be limited by the effort required to specify the fuzzy inference rules, whose number grows rapidly with the number of input variables and membership functions (MFs). To this purpose, we propose the Fuzzy INDices (F-IND) framework as the foundation for an FD system; F-IND automatically generates the fuzzy rules from the specification of qualitative best and worst cases on the MFs of each input variable, which greatly reduces the burden on model design. This article presents the mathematical model of F-IND, its implementation, and its application to the specific case of FD of electric motors, evaluating its computational performance and the accuracy in both simulated and experimental settings.

Wearable sensor-based fuzzy decision-making model for improving the prediction of human activities in rehabilitation

Sports actions are commonly recurrent due to the abnormal dynamic human activities. Detecting physical injuries based on the actions of the sportsperson helps to fasten rehabilitation treatments. Rehabilitation relies on the precise detection of activities and continuous monitoring of the actions of the sportsperson. In this paper, wearable sensor-based fuzzy decision-making (FDM) model is introduced for improving the prediction accuracy of different activities of the sportsperson. This model relies on altering sensor data aggregation and processing them using classification conditions for improving the prediction accuracy. The decision-making is performed by linearly classifying independent membership functions for different aggregation time and inputs. The combined processing of the inputs and time-based actions using independent decisions helps to improve the prediction accuracy of 93.3% with 26.081 ms decision time compared to conventional algorithms.

AN FLC-PSO ALGORITHM-CONTROLLED MOBILE ROBOT

The ineffectiveness of the wall-following robot (WFR) performance indicated by its surging movement has been a concerning issue. The use of a Fuzzy Logic Controller (FLC) has been considered to be an option to mitigate this problem. However, the determination of the membership function of the input value precisely adds to this problem. For this reason, a particular manner is recommended to improve the performance of FLC. This paper describes an optimization method, Particle Swarm Optimization (PSO), used to automatically determinate and arrange the FLC’s input membership function. The proposed method is simulated and validated by using MATLAB. The results are compared in terms of accumulative error. According to all the comparative results, the stability and effectiveness of the proposed method have been significantly satisfied.

Air in industrial premises

The article considers a method for increasing the stability of air parameters in a limited space of the general production process using simulation methods in the environments of SolidWorks Flow Simulation HVAC Module Add-In and Matlab Simulink and Fuzzy Logic Toolbox. To select the simulation method, the models of Boussinesq, Spalart - Almaras, k - ω and k - ε; models, Reynolds, and the equations describing these models are considered. It is established that the most effective for the problem is the Navier-Stokes equation averaged by Reynolds. For the calculated ventilation system, as a result of simulation, we obtained the distribution of temperatures, air velocities, and temperatures of enclosing surfaces, floors, and objects. The results obtained were used to create a fuzzy controller. We obtained the set term and membership functions for input and output variables for four zones. The hierarchical structure consisting of three levels was used to implement the control process. Setting the FC parameters to the desired parameters is achieved by changing the dimension of the terms of the sets, the number and type of the membership function. An analytical description of the control method based on fuzzy logic is obtained. The control process is being carried out continuously in tracking mode. The results of experiments are submitted. The simulation showed that it is possible to stabilize the parameters in a given temperature range with a specified error.

Fuzzy-Based Model for Predicting Strength of Geogrid-Reinforced Subgrade Soil with Optimal Depth of Geogrid Reinforcement

A fuzzy logic (FL)–based modeling approach is employed for geogrid-reinforced subgrade soil of unpaved roads. A review of the literature reveals that fuzzy logic has not been used for predicting the behavior of geogrid-reinforced subgrade. This paper presents FL-based two models with fuzzy Triangular and Gaussian membership functions for input and output variables. It consists of eight input parameters/factors, namely, reinforced/unreinforced section, depth of reinforcement, liquid limit, plastic limit, plasticity index, optimum moisture content, maximum dry unit weight, and soaked/unsoaked condition, and California bearing ratio (CBR) as an output parameter. The fuzzy rules are deduced from the experimental data. The laboratory CBR tests were performed on the subgrade soil reinforced with geogrid. The precision of models was examined by comparing the predicted CBR values with the experimental CBR values for Triangular and Gaussian membership functions. The sensitivity analysis reflects a set of dominant parameters. The results indicated a significant improvement in the CBR value of geogrid-reinforced subgrade soil due to the inclusion of geogrid. The range for optimal depth of geogrid reinforcement is found to be 36 to 60% of the thickness of the soil layer. The potentialities of FL were found to be satisfactory.

PENYUSUNAN DAN PENERAPAN METODE REGRESSION ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (RANFIS) UNTUK ANALISIS DATA KURS IDR/USD

The exchange rate of rupiah is one of the important prices in an open economy because the exchange rate can be used as a tool to measure the economic condition of a country. The movement of the rupiah exchange rate affected the Indonesian economy, maintaining the stability of the rupiah exchange rate became an important thing to do. In an effort to maintain the stability of the rupiah exchange rate, the factors that influence it must first be identified. Several factors affect the IDR / USD exchange rate, namely the large trade price index, foreign exchange reserves, money supply and interest rates. In this study, the Regression Adaptive Neuro Fuzzy Inference System (RANFIS) method was used to analyze the effect of predictor variables on IDR / USD exchange rates. The optimal RANFIS model is strongly influenced by three things, namely the determination of input predictor variable, membership functions, and number of clusters. Determination of the optimal RANFIS model is measured based on the smallest MAPE in-sample. Based on empirical studies applied to predictor variables on IDR / USD exchange rates, it was found that the RANFIS model was optimal, namely with 3 predictor variable inputs consisting of large trade price index variables, money supply and interest rates; with the gauss membership function; 2 clusters and rules produce an MAPE in-sample of 1.93% and an MAPE out-sample of 2.68%, so the performance of the RANFIS model has a very good level of accuracy.

Optimized fuzzy self-tuning PID controller design based on Tribe-DE optimization algorithm and rule weight adjustment method for load frequency control of interconnected multi-area power systems

The reliable load frequency control (LFC) is addressed as one of the most important services in the modern electric power system operation and planning. Since the power systems have various structural and non-structural uncertainties, using control methods with fixed parameters may not yield the optimal performance of the system. Thus, in this research, a novel fuzzy PID controller is introduced to LFC for interconnected multi-area power systems in parametric uncertainties as well as external disturbances existence. The proposed algorithm adjusts the scaling factors and the modal parameters of input and output membership functions as well as the weights of fuzzy PID controller rule values. According to the transient response of the area control error (ACE) variable partitioning, the fuzzy rule weight values are obtained. Furthermore, in order to enhance the quality of the response, the scaling factors and the modal parameters of the input and output membership functions of fuzzy PID controllers are optimized by Tribe-DE (TDE) algorithm. The method is examined on the two and three area interconnected power systems at several conditions and an Integral of Time multiplied Absolute Error (ITAE) less than 0.0108 as well as an absolute maximum undershoot less than 0.0210 Hz for Δf1 regulation are obtained in the two area interconnected power system. Good transient behavior, disturbance rejection capability and insensitivity to parameter changes are advantages of the proposed controller.

An artificial intelligence approach for concrete hardened property estimation

An alternative method using Artificial Intelligence (AI) to predict the 28-day strength of concrete from its primary ingredients is presented in this research. A series of 424 data samples collected from a previous study were employed for developing, testing, and validation of Adaptive Neuro-Fuzzy Inference System (ANFIS) models. Seven mix parameters, namely Cement, Blast Furnace Slag, Fly Ash, Water, Superplasticizer, Coarse Aggregate, and Fine Aggregate were used as the inputs of the models while the output was the 28-day compressive strength of concrete. In the first step, different models with various input membership functions were explored and compared to obtain an optimal ANFIS model. In the second step, that model was utilized to predict the compressive strength value for each concrete sample, and to compare with those obtained from the compressive test in laboratory. The results showed that the selected ANFIS model can be used as a reliable tool for predicting the compressive strength of concrete with Root Mean Squared Error values of 5.97 MPa and 7.73 MPa, respectively, for the training and test sets. In addition, the sensitivity analysis results revealed that the accuracy of the proposed model improved with an increase in the number of input parameters/variables.
 Keywords:
 artificial intelligence; adaptive neuro-fuzzy inference system; concrete strength; sensitivity analysis.

An Improved and Adaptive Approach in ANFIS to Predict Knee Diseases

Artificial intelligence is emerging as a persuasive tool in the field of medical science. This research work also primarily focuses on the development of a tool to automate the diagnosis of inflammatory diseases of the knee joint. The tool will also assist the physicians and medical practitioners for diagnosis. The diseases considered for this research under inflammatory category are osteoarthritis, rheumatoid arthritis and osteonecrosis. A five-layer adaptive neuro-fuzzy (ANFIS) architecture was used to model the system. The ANFIS system works by mapping input parameters to the input membership functions, input membership functions are mapped to the rules generated by the ANFIS model which are further mapped to the output membership function. A comparative performance analysis of fuzzy system and ANFIS system is also done and results generated shows that the ANFIS system outperformed fuzzy system in terms of testing accuracy, sensitivity and specificity.

Comparison of Mamdani and Sugeno Inference Methods in Calorie Burn Calculation for Activity Using Treadmill

This research provides a comparison between the performance of Mamdani and Sugeno Fuzzy Inference System in Calorie Burn calculation for treadmill workout exercise. The results are compared against the calculation calorie burn value using traditional formula. The objective of this results to get a system which provides calorie burn results closer to the traditionally calculated value. The Mamdani and Sugeno systems are designed with two input membership functions namely Incline and Speed and one output membership function called Calorie Burn as used in the traditional calculation method. The fuzzy rule table is designed using 7 Membership function for Incline and speed inputs and 9 Membership function for calorie burn output. The Mamdani and Sugeno system are tested with the same fuzzy rules for better comparison. The results are tabulated with theoretical values compared with Mamdani and Sugeno system for 5 incline levels and 7 speed levels. It can be observed that the Sugeno system results are closer to the traditional calculation results. This is shows that the sugeno system works better than Mamdani system for linear system. Also it depicts the calculated values and hence it can be used as a replacement for tedious mathematical analysis.

Learning rules for Sugeno ANFIS with parametric conjunction operations

The paper presents a Sugeno Adaptive Neuro-Fuzzy Inference System with parametric conjunction operations architecture, ANFIS-CX. The advantages of using parametric conjunction operations in fuzzy models are discussed, and learning rules for system identification with such operations are proposed. These learning strategies can include steepest descent gradient, differential evolution and least square estimation algorithms for tuning antecedent, conjunction, and consequent parameters, respectively. The results of system identification by parameter tuning of conjunction operations in addition to or instead of parameter tuning of the input membership functions are presented. Simulation results show that parameter training in conjunction operations, composed of four basic t-norms, significantly improves the approximation capability of fuzzy models.

A Simple Tuning Algorithm of Augmented Fuzzy Membership Functions

The successful application of fuzzy control depends to a large extent on the parameters of some subjective decisions, such as fuzzy membership function (MF). Fuzzy logic controller (FLC) implementing augmented output MFs as compare to input MFs is presented to improve the accuracy, robustness, and performance of the system. The best possible combination of input and output MFs is introduced to distribute the uniform input MFs and augmented output MFs in the treatise. The simulation of the 2-Inputs 1-Output Fuzzy Control System is performed in many nonlinear processes. Then, the experimental outcomes of the uniformly and augmented distributed output MFs are compared under similar circumstances. The experimental outcomes are in a virtuous covenant with the simulation outcomes. The experimental outcomes show that the root mean square error (RMSE) is reduced around 75.3% and bringing down the relative error to the acceptable range (≤±10%). The control accuracy is improved and the robustness is boosted by reducing the RMSE through the FLC with augmented-distributed output MFs. Moreover, the cost and energy efficiency in any fuzzy system will be improved by implementing the augmented-distributed output MFs using the best possible combination of input and output MFs.

Improvement of Rule Generation Methods for Fuzzy Controller

This paper proposes fuzzy modeling using obtained data. Fuzzy system is known as knowledge-based or rule-bases system. The most important part of fuzzy system is rule-base. One of problems of generation of fuzzy rule with training data is inconsistence data. Existence of inconsistence and uncertain states in training data causes high error in modeling. Here, Probability fuzzy system presents to improvement the above challenge. A zero order Sugeno fuzzy model used as fuzzy system structure. At first by using clustering obtains the number of rules and input membership functions. A set of candidate amounts for consequence parts of fuzzy rules is considered. Considering each pair of training data, according which rules fires and what is the output in the pair, the amount of probability of consequences candidates are change. In the next step, eligibility probability of each consequence candidate for all rules is determined. Finally, using these obtained probability, two probable outputs is generate for each input. The experimental results show superiority of the proposed approach rather than some available well-known approaches that makes reduce the number of rule and reduce system complexity.

Benchmark of an intelligent fuzzy calculator for admissible estimation of drawbar pull supplied by mechanical front wheel drive tractor

This paper proposes a calculator for estimation of drawbar pull supplied by mechanical front wheel drive tractor based on nominal input variable of tractor driving mode in two-wheel drive (2WD) and four-wheel drive (4WD), and numeral input variables of tractor weight (53.04–78.45 kN) and slip of driving wheels (1.4–15.1%) utilizing intelligent fuzzy systems. The systems were developed by means of various input membership functions, output membership functions, defuzzification methods, and training cycles. The prominent developed system for estimation of the drawbar pull yielded a user-friendly intelligent fuzzy calculator with admissible accuracy (coefficient of determination = 0.993). Data obtained from the calculator revealed increasing nonlinear trend of the drawbar pull in range of 12.9–57.5 kN as concurrent augment of slip of the wheels and tractor weight, for 2WD mode. In case of the 4WD mode, it nonlinearly raised from 12.8 to 77.7 kN. Therefore, effect of the slip and weight on the drawbar pull was found synergetic. Moreover, the drawbar pull ranges elucidated that the drawbar pull proliferated as the 4WD mode was employed rather than the 2WD mode. Generally, benchmark of the prominent developed intelligent fuzzy system, not only provide simple calculator with the widest applicability for different tractor models, but also produces added values in enrichment of realization level in domain of tractor drawbar pull concepts.

METHODOLOGY FOR THE MINING OF HUMAN RESOURCES IN THE DIGITAL ECONOMY

The In this article, a systematic methodology for analyzing and assessing the effectiveness of human resources based on fuzzy sets using big data technologies is used. Based on our research, we analyzed the big data construction method for our chosen approach using Industry 4.0.For the selected fuzzy sets, a set of sequence of procedures in the sequence of the method for assessing the effectiveness of human resources have been identified. Input and output membership functions for data mining have been developed.This article discusses process of building rules of fuzzy logic that allowed us to determine the degree of truth for each condition.The relevance achieved through the development of a methodology that includes eight procedures required for a comprehensive assessment of the economic efficiency of human resources. In this article, an approach to assessing the normative or average values of the performance of official duties by employees of an enterprise in many specialties, educational levels, levels of management, as well as taking into account the description of many positions, descriptions of compliance and interchangeability of positions, assessment of additional characteristics of employees and a description of many additional tasks and their characteristics is presented. The article presents a structural data-mining model for personnel assessment. The results of modeling the assessment of human resources is presented.

Design of Fuzzy Membership Functions for Predicting Student’s Knowledge Performance

A Fuzzy logic controller is a problem-solving control system that provides means for representing approximate knowledge. The output of a fuzzy controller is derived from the fuzzifications of crisp (numerical) inputs using associated membership functions. The crisp inputs are usually converted to the different members of the associated linguistic variables based on their respective values. This point is evident enough to show that the output of a fuzzy logic controller is heavily dependent on its memberships of the different membership functions, which can be considered as a range of inputs [4]. Input membership functions can take various forms trapezoids, triangles, bell curves, singleton or any other shape that accurately enables the distribution of information within the system, in as much as the shape provides a region of transition between adjacent membership functions.