Tuning Of Membership Functions Research Articles

Nature-inspired optimal tuning of input membership functions of fuzzy inference system for groundwater level prediction

We present a novel regression algorithm that combines a Fuzzy Inference System (FIS) with a nature-inspired algorithm to predict variations in GroundWater Levels (GWLs). Initially, we considered several input features, including precipitation, temperature, evaporation, relative humidity, soil type, and GWL Lag. A feature importance analysis using regression tree ensemble learning reveals GWL lag as the most relevant feature and soil type as the least relevant. We eliminate features with low importance scores (soil type, temperature, and evaporation) to improve the computational efficiency. Our approach leverages Fuzzy C-Means (FCM) clustering to develop a Sugeno-type FIS with definite clusters. The dataset is clustered based on feature similarity, and Gaussian membership functions are assigned to each cluster. The mapping of each point is controlled by the range and standard deviation of the Gaussian membership functions. We train the model keeping 70% of the dataset, iteratively tuning the parameters of the Gaussian membership function for all clusters through the Invasive Weed Optimization (IWO) algorithm. The performance of the model is then evaluated using the remaining 30% of the datasets. The F-IWO-GWL model accurately predicts GWL fluctuations, achieving a high correlation coefficient (R = 0.89), low normalized root mean square error (nRMSE = 0.18), and minimal bias (bias = 0.08). A comparative analysis involving seventeen benchmark algorithms reveals the superior performance of our algorithm. To ensure a fair comparison, we calculated key metrics including Akaike’s Information Criterion (AIC), Bayesian Information Criterion (BIC), and Corrected AIC (AICc). The F-IWO-GWL algorithm exhibits the lowest values for AIC, BIC, and AICc among all tested algorithms, suggesting the best goodness-of-fit. This study provides a robust approach for predicting GWL fluctuation, applicable to various groundwater management scenarios. It offers valuable insights for policymakers and stakeholders involved in groundwater management, aiding informed decision-making.

Interval Type 2 Adaptive Neuro-Fuzzy Inference System-Based Artificial Pacemaker Design and Stability Analysis.

This paper presents the design and simulation of an Interval type 2 fuzzy system (IT2FS) based, adaptive neuro-fuzzy inference system (ANFIS) pacemaker controller in MATLAB. After designing the type 1 fuzzy logic model, the stability of the designed system has been verified in the time-domain (unit step response). In previous works, the type 1 (IT1FS) model step response was analyzed. They are compared with the other proportional integral derivative (PID) and fuzzy models that only least-square-estimation and the backpropagation algorithms are used for tuning membership functions (MF) and generation of type 1 fis (fuzzy inference system) file. At current work, fuzzy C means (FCM) method shows better results than other methods have been used. The pacemaker controller determines the pacing rate and adjusts the heart rate of the patient for the reference input signal. The rise-time, overshoot and settling-time have been improved significantly.

Optimizing Membership Function Tuning for Fuzzy Control of Robotic Manipulators Using PID-Driven Data Techniques

In this study, a method for optimizing membership function tuning for fuzzy control of robotic manipulators using PID-driven data techniques is presented. Traditional approaches for designing membership functions in fuzzy control systems often rely on the experience and knowledge of the system designer, which can lead to suboptimal performance. By utilizing data collected from a PID control system, the proposed method aims to enhance the precision and controllability of robotic manipulators through improved fuzzy logic control. A Mamdani-type fuzzy logic controller was developed and its performance was simulated in Simulink, demonstrating the effectiveness of the proposed optimization technique. The results indicate that the method can outperform conventional P control systems in terms of overshoot reduction while maintaining comparable transient response specifications. This research highlights the potential of the PID-driven data-based approach for optimizing membership function tuning in fuzzy control systems and offers valuable insights for the development and evaluation of fuzzy logic control in robotic manipulators. Future work may focus on further optimization of the tuning process, evaluation of system robustness under various operating conditions, and exploring the integration of other artificial intelligence techniques for improved control performance.

Output Linearization of Single-Input Single-Output Fuzzy System to Improve Accuracy and Performance

For fuzzy systems to be implemented effectively, the fuzzy membership function (MF) is essential. A fuzzy system (FS) that implements precise input and output MFs is presented to enhance the performance and accuracy of single-input single-output (SISO) FSs and introduce the most applicable input and output MFs protocol to linearize the fuzzy system’s output. Utilizing a variety of non-linear techniques, a SISO FS is simulated. The results of FS experiments conducted in comparable conditions are then compared. The simulated results and the results of the experimental setup agree fairly well. The findings of the suggested model demonstrate that the relative error is abated to a sufficient range (≤ ± 10%) and that the mean absolute percentage error (MPAE) is reduced by around 66.2%. The proposed strategy to reduce MAPE using an FS improves the system’s performance and control accuracy. By using the best input and output MFs protocol, the energy and financial efficiency of every SISO FS can be improved with very little tuning of MFs. The proposed fuzzy system performed far better than other modern days approaches available in the literature.

A Hybrid Systematic Review Approach on Complexity Issues in Data-Driven Fuzzy Inference Systems Development

The data-driven approach is popular to automate learning of fuzzy rules and tuning membership function parameters in fuzzy inference systems (FIS) development. However, researchers highlight different challenges and issues of this FIS development because of its complexity. This paper evaluates the current state of the art of FIS development complexity issues in Computer Science, Software Engineering and Information Systems, specifically: 1) What complexity issues exist in the context of developing FIS? 2) Is it possible to systematize existing solutions of identified complexity issues? We have conducted a hybrid systematic literature review combined with a systematic mapping study that includes keyword map to address these questions. This review has identified the main FIS development complexity issues that practitioners should consider when developing FIS. The paper also proposes a framework of complexity issues and their possible solutions in FIS development.

Incipient short-circuit fault diagnosis of lithium-ion batteries

Abstract Diagnosing incipient short circuit (SC) of on-board lithium-ion cells is of great importance for safety operation, because it can prevent further deterioration such as spontaneous thermal runaway. Considering equivalent circuit models (ECMs) are currently the most used models for executing on-line battery state and parameter estimation, hence the purpose of this study is to propose a general battery incipient SC detection method through the commonly used ECMs. Model generality, design generality and implementation generality are the main design criteria. In this paper, inspired by the concept of Takagi-Sugeno fuzzy system, a weighting function self-regulating non-linear robust state and fault estimator that is designed for battery SC detection is proposed. Namely, the slowly changing characteristic of battery state of charge (SOC) is fully taken into account to construct a weighting function self-regulating mechanism among different design segments. Genetic algorithm has been used for the membership function tuning. Therefore, incipient SC detection is addressed from the perspective of fault estimation. The absolute estimation error of battery SOC after the SC fault occurrence is smaller than 0.01 regardless of the SC resistance values. Furthermore, considering the estimated fault signal is usually corrupted with noise in reality, a statistical technique, namely Cumulative Sum, is employed to detect the tiny change of the signal due to the incipient fault. Theoretical and methodological contributions are the main aims of this research work. Intensive numerical simulations with real experimental data have verified the effectiveness of the proposed incipient SC detection method.

Bee Colony Optimization metaheuristic for fuzzy membership functions tuning

The successfulness of the application of fuzzy logic to real-life problems depends on a number of parameters, such as the number and shapes of fuzzy membership functions, which are usually defined upon intuition or the subjective knowledge of relevant experts. One way to improve the performance of the fuzzy reasoning model is to use heuristic or metaheuristic techniques in order to optimize the shapes of membership functions. In this paper, the Bee Colony Optimization (BCO) algorithm is applied as a tool suitable for the specified optimization problem. The BCO belongs to the group of nature-inspired metaheuristics. The purpose of this paper is to present and discuss a strategy for the adjustment of fuzzy logic membership functions using the variant of the BCO algorithm based on the improvement of complete solutions and show its real-life application to the problem of the estimation of freight train energy consumption. According to the obtained results, it can be concluded that the precision of the developed fuzzy reasoning model is significantly increased after tuning membership functions by the BCO.

Genetic optimised serial hierarchical fuzzy classifier for breast cancer diagnosis

Accurate early-stage medical diagnosis of breast cancer can improve the survival rates and fuzzy rule-base system (FRBS) has been a promising classification system to detect breast cancer. However, the existing classification systems involves large number of input variables for training and produces a large number of fuzzy rules, which lead to high complexity and barely acceptable accuracy. In this paper, we present a genetic optimised serial hierarchical FRBS, which incorporates lateral tuning of membership functions and optimisation of the rule base. The serial hierarchical structure of FRBS allows selecting and ranking the input variables, which reduces the system complexity and distinguish the importance of attributes in datasets. We conduct an experimental study on Original Wisconsin Breast Cancer Database and Wisconsin Breast Cancer Diagnostic Database from UCI Machine Learning Repository, and show that the proposed system can classify breast cancer accurately and efficiently.

Genetic optimised serial hierarchical fuzzy classifier for breast cancer diagnosis

Accurate early-stage medical diagnosis of breast cancer can improve the survival rates and fuzzy rule-base system (FRBS) has been a promising classification system to detect breast cancer. However, the existing classification systems involves large number of input variables for training and produces a large number of fuzzy rules, which lead to high complexity and barely acceptable accuracy. In this paper, we present a genetic optimised serial hierarchical FRBS, which incorporates lateral tuning of membership functions and optimisation of the rule base. The serial hierarchical structure of FRBS allows selecting and ranking the input variables, which reduces the system complexity and distinguish the importance of attributes in datasets. We conduct an experimental study on Original Wisconsin Breast Cancer Database and Wisconsin Breast Cancer Diagnostic Database from UCI Machine Learning Repository, and show that the proposed system can classify breast cancer accurately and efficiently.

Meta heuristic active and semi-active control systems of high-rise building

This study focuses on the application of dampers installed in a structure to control the seismic responses of the building. Fuzzy logic controller is applied to estimate control force and voltage of ATMD and MR damper. To find out a suitable fuzzy logic controller which reduces the responses of structure, it is essential to tune membership functions, extract appropriate rule base. The dynamic parameters of TMD is very effective to decrease the responses of structure. The observer-teacher-learner-based optimisation (OTLBO) algorithm is used to optimise TMD parameters and fuzzy logic controller. The results show that the optimised models by this algorithm can effectively decrease the peak seismic response of the building.

Meta heuristic active and semi-active control systems of high-rise building

This study focuses on the application of dampers installed in a structure to control the seismic responses of the building. Fuzzy logic controller is applied to estimate control force and voltage of ATMD and MR damper. To find out a suitable fuzzy logic controller which reduces the responses of structure, it is essential to tune membership functions, extract appropriate rule base. The dynamic parameters of TMD is very effective to decrease the responses of structure. The observer-teacher-learner-based optimisation (OTLBO) algorithm is used to optimise TMD parameters and fuzzy logic controller. The results show that the optimised models by this algorithm can effectively decrease the peak seismic response of the building.

Cluster-Based Membership Function Acquisition Approaches for Mining Fuzzy Temporal Association Rules

In real-world applications, transactions are typically represented by quantitative data. Thus, fuzzy association rule mining algorithms have been proposed to handle these quantitative transactions. In addition, items generally have certain lifespans or temporal periods in which they exist in a database. Therefore, fuzzy temporal association rule mining algorithms have also been proposed in the literature. A key factor in the acquisition of fuzzy temporal association rules (FTARs) is the design of appropriate membership functions. Because current approaches have been designed to generate membership functions for mining fuzzy association rules (FARs) in market-basket analysis, in this paper, we propose a membership function tuning mechanism for a fuzzy temporal association rule mining algorithm. The proposed approach modifies an existing cluster-based method to generate unique membership functions that are specifically tailored to each item in a dataset. Two factors are utilized to decide the appropriate membership functions of each item: (1) the density similarity among intervals corresponding to the density similarity within intervals, and (2) the information closeness within an interval corresponding to the similarity in the number of data points between intervals. A parameter θ is used to indicate the relative importance of these two factors. As a result, the membership functions are generated based on the quantitative ranges of individual items, and the generated membership functions of items are different in terms of the values of each interval and the number of intervals. The generated membership functions are subsequently used in a fuzzy temporal association rule mining algorithm. Computational experiments were conducted on both a synthetic dataset and a real-world one to demonstrate the effectiveness of the proposed approach.

Crisp set tuning of membership functions in fuzzy logic inference systems by probability density functions.

Crisp set tuning of membership functions in fuzzy logic inference systems by probability density functions.

General 3-D Type-II Fuzzy Logic Systems in the Polar Frame: Concept and Practice

This paper deals with the concept and application of general three-dimensional (3-D) type-2 fuzzy logic systems in the polar frame (G3DT2FLS). 1 1 Because many acronyms are used in this paper, they are summarized in Table I for the convenience of the reader. We focused on the automatic membership function (MF) Generator, general type-2 polar fuzzy membership, new geometric operators in polar frame, inference consisting of fuzzy 3-D polar rules and antecedents/consequents $\theta$ -slice and $\alpha$ -planes. The cubic smoothing spline is introduced to generate the upper and lower MFs according to the information theory. Three indices of compactness, smoothness, and entropy are employed for tuning of MFs. A measure of ultrafuzziness, min, max, equal, and reduced ultrafuzziness are suggested for the polar method. Additionally, the set theoretic operations of 3-D type-2 fuzzy sets in the polar frame are discussed. We also prove the join, meet, centroid, and type-reduction operations in the polar frame. Several rule sets are given to show the usefulness and complexity of the proposed method. The performance of the partial general 3D polar type-2 fuzzy logic system showed linear growth as the number of rules was increased. Computation time tests showed that the algorithm reduces the computation time by a maximum of 67% compared with discrete MF and is shown by an extreme 98% for the geometric procedure. These results indicate significant improvements in computation time for the spline interpolation over the existing methods.

A Hybrid ANFIS-ABC Based MPPT Controller for PV System With Anti-Islanding Grid Protection: Experimental Realization

This paper introduces a novel control system with maximum power point tracker (MPPT) for the photovoltaic system with grid integration. Hybrid adaptive neuro-fuzzy inference system (ANFIS) and artificial bee colony (ABC) algorithm employed to optimize the membership function. Hence, for minimizing the root mean square error (RMSE), this controls the SEPIC-based MPPT algorithm to achieve rapid PV power tracking. The system performance is improved by fuzzy logic control (FLC), which generates the switching signal to the power switches of the inverter. A dSPACE (DS1104) control board employed for experimental validation of MPPT and inverter control strategies. The novelty of the proposed hybrid MPPT controller is the optimal tuning of ANFIS membership function with the ABC algorithm and been neither discussed before for PV power applications. The experimental responses completely validate the reliability of the PV grid integration with anti-islanding protection. The recentness of this research work is PV MPPT functioning using the hybrid ANFIS-ABC-based algorithm, been not described practically by any researchers in the past works.

Real-time Video Quality Assessment for Analog Television Based on Adaptive Fuzzy Membership Function Tuning

Real-time VQA (Video Quality Assessment) is an important part in the effort to build tracking antenna system especially for analog TV. In this case, VQA must work in real-time to assess the video clarity level. VQA assessment results are valuable information for the decision-making process. Thus, the antenna can rotate automatically looking for the ideal direction without user’s control. In addition, the video clarity level on the TV screen can reach optimum according to the user's wishes. The biggest challenge to VQA is, VQA must be able to assess the video clarity level according to the user’s visual perception. Therefore, in this study, the MOS-VQS (Mean Opinion Score-Video Quality Subjective) was used as a visual perception approach. In addition, Adaptive FIS (Fuzzy Inference System) with membership function tuning was implemented for decision making. This was conducted as an effort to build a reliable real-time VQA. The test results show that real-time VQA that has been built has a good performance. This is shown from the average accuracy percentage of the lowest assessment reached 77.2% and the highest reached 88.2%.

Investigating the Impact of Load Profile Attributes on Demand Response Exchange

This paper investigates the impact of load profiling attributes on the demand response exchange (DRX) mechanism. Modeling of demand response (DR) seller bids in traditional approach/model includes a customer willingness factor assigned in a random/arbitrary manner. The proposed model of DR bids takes into account, the load attributes such as utilization and availability factors. The criticality and willingness of the DR load/customer are embedded into seller market bids through utilization and availability factor, respectively. Various cost models have been developed to emulate the possible customer behavior in DR cost modeling. However, modeling of indistinct and uncertain nature of customer behavior is a complex issue in real-time consideration. Therefore, this paper also presents a fuzzy inference system (FIS) for considering the customer load profile attributes in DR bids. In addition, parameter tuning of fuzzy membership functions is also carried out in this paper using heuristic optimization techniques to improve the performance of FIS in DRX clearing. The proposed methodology with load profile attributes is simulated considering various load types across different load sectors. Simulation results of proposed tuned FIS-based DRX clearing are compared with other nonfuzzy models, conventional models without load attributes and untuned FIS system to demonstrate the effectiveness of tuned FIS with load profile attribute consideration in DRX clearing.

Line Follower Robot Optimization based Fuzzy Logic Controller Using Membership Function Tuning

The study was aimed to measure the performance of Fuzzy Logic Controller (FLC) on Line Follower Robot (LFR). FLC output is a deviation value of Pulse Width Modulation (PWM) to determine the rotational speed of the left and the right wheel. As input variables are current and previous line sensors. Tuning was applied to input and output variables in each membership function (MF) to conduct the best performance. This study used triangular membership function that consists of three MF. Mamdani Fuzzy Inference System (FIS) is used using nine rules. The result obtains that after MF tuning, the performance of the LFR settling time is 0.63s faster compare to that without tuning.

Self-adaptive type-1/type-2 hybrid fuzzy reasoning techniques for two-factored stock index time-series prediction

Considerable research outcomes on stock index time-series prediction using classical (type-1) fuzzy sets are available in the literature. However, type-1 fuzzy sets cannot fully capture the uncertainty involved in prediction because of its limited representation capability. This paper fills the void. Here, we propose four chronologically improved methods of time-series prediction using interval type-2 fuzzy sets. The first method is concerned with prediction of the (main factor) variation time-series using interval type-2 fuzzy reasoning. The second method considers secondary factor variation as an additional condition in the antecedent of the rules used for prediction. Another important aspect of the first and the second methods is non-uniform partitioning of the dynamic range of the time-series using evolutionary algorithm, so as to ensure that each partition includes at least one data point. The third method considers uniform partitioning without imposing any restriction on the number of data points in a partition. The partitions are here modeled by type-1 fuzzy sets, if there exists a single block of contiguous data, and by interval type-2 fuzzy sets, if there exists two or more blocks of contiguous data in a partition. The fourth method keeps provision for tuning of membership functions using recent data from the given time-series to influence the prediction results with the current trends. Experiments undertaken confirm that the fourth technique outperforms the first three techniques and also the existing techniques with respect to root-mean-square error metric.

Fuzzy Sets in Dynamic Adaptation of Parameters of a Bee Colony Optimization for Controlling the Trajectory of an Autonomous Mobile Robot.

A hybrid approach composed by different types of fuzzy systems, such as the Type-1 Fuzzy Logic System (T1FLS), Interval Type-2 Fuzzy Logic System (IT2FLS) and Generalized Type-2 Fuzzy Logic System (GT2FLS) for the dynamic adaptation of the alpha and beta parameters of a Bee Colony Optimization (BCO) algorithm is presented. The objective of the work is to focus on the BCO technique to find the optimal distribution of the membership functions in the design of fuzzy controllers. We use BCO specifically for tuning membership functions of the fuzzy controller for trajectory stability in an autonomous mobile robot. We add two types of perturbations in the model for the Generalized Type-2 Fuzzy Logic System to better analyze its behavior under uncertainty and this shows better results when compared to the original BCO. We implemented various performance indices; ITAE, IAE, ISE, ITSE, RMSE and MSE to measure the performance of the controller. The experimental results show better performances using GT2FLS then by IT2FLS and T1FLS in the dynamic adaptation the parameters for the BCO algorithm.