Feature Selection Based on Adaptive Fuzzy Membership Functions

Neuro-fuzzy(NF)networks are adaptive fuzzy inference systems(FIS)and have been applied to feature selection by some researchers.However,their rule number will grow exponentially as the data dimension increases.On the other hand,feature selection algorithms with artificial neural networks(ANN)usually require normalization of input data,which will probably change some characteristics of original data that are important for classification.To overcome the problems mentioned above,this paper combines the fuzzification layer of the neuro-fuzzy system with the multi-layer perceptron(MLP)to form a new artificial neural network.Furthermore,fuzzification strategy and feature measurement based on membership space are proposed for feature selection. Finally,experiments with both natural and artificial data are carried out to compare with other methods,and the results approve the validity of the algorithm.

A modeling approach for fuzzy programming with echelon form membership functions

In order to solve fuzzy mathematical programming with soft constraints,the initial models must first be converted into crisp models.Membership functions are employed to describe the fuzzy right-hand side parameters needed to achieve this conversion.In some cases,echelon form membership functions(EFMFs)are required to depict the actual fuzzy situation.However,due to their discrete properties,fuzzy programming problems with such membership functions cannot be modeled by traditional methods.Motivated by these challenges,this paper introduces a novel absolute value representation modeling approach to formulate fuzzy programming using EFMFs.This approach can translate a discrete model to a continuous one which can then be easily solved.Finally,by means of a numerical example,the effectiveness of our new approach is demonstrated.

Energy Price Forecasting Through Novel Fuzzy Type-1 Membership Functions

Electricity price forecasting is a subset of energy and power forecasting that focuses on projecting commercial electricity market present and future prices.Electricity price forecasting have been a critical input to energy corporations’strategic decision-making systems over the last 15 years.Many strategies have been utilized for price forecasting in the past,however Artificial Intelligence Techniques(Fuzzy Logic and ANN)have proven to be more efficient than traditional techniques(Regression and Time Series).Fuzzy logic is an approach that uses membership functions(MF)and fuzzy inference model to forecast future electricity prices.Fuzzy c-means(FCM)is one of the popular clustering approach for generating fuzzy membership functions.However,the fuzzy c-means algorithm is limited to producing only one type of MFs,Gaussian MF.The generation of various fuzzy membership functions is critical since it allows for more efficient and optimal problem solutions.As a result,for the best and most improved results for electricity price forecasting,an approach to generate multiple type-1 fuzzy MFs using FCM algorithm is required.Therefore,the objective of this paper is to propose an approach for generating type-1 fuzzy triangular and trapezoidal MFs using FCM algorithm to overcome the limitations of the FCM algorithm.The approach is used to compute and improve forecasting accuracy for electricity prices,where Australian Energy Market Operator(AEMO)data is used.The results show that the proposed approach of using FCM to generate type-1 fuzzy MFs is effective and can be adopted.

Comprehensive Evaluation of New Maize Varieties for Grain and Fodder Based on Membership Function Method

To screen new maize(Zea mays L.)varieties suitable for food and fodder dual-purpose in Du'an Yao Autonomous County of Guangxi,the agronomic characters,yield and quality indexes of 12 new maize varieties were measured,and the correlation between various indexes were analyzed,and the comprehensive performance of tested varieties was evaluated by membership function method.The results showed that Guidan 671 had the highest grain yield and whole-plant biomass at 10908 and 49965 kg/hm^(2),respectively,and the second was Zhaoyu 215 with a grain yield and whole-plant biomass of 10086 and 47175 kg/hm^(2),respectively.Grain yield was highly significantly positively correlated with ear diameter and 100-grain weight(P<0.01),and significantly correlated with whole-plant biomass,starch content,ear length and grain number per row(P<0.05);and the whole-plant biomass was highly significantly correlated with the number of grains per row(P<0.01),and significantly correlated with starch content,panicle length,plant height and panicle height(P<0.05).The comprehensive performance scores of the tested varieties from high to low were Guidan 671,Zhaoyu 215,Guidan 669,Guidan 6208,Guidan 666,Guidan 6205,Guidan 660,Guidan 6203,Guidan 6206,Guidan 162,Guidan 668 and Guidan 673.According to the values of membership function and combined with various indexes,Guidan 671 and Zhaoyu 215 had good comprehensive performance,and could be used as the first choice for food and fodder dual-purpose maize varieties in Du'an Yao Autonomous County.

A New Approach for Solving Fuzzy Linear Multi-Criterion Problems: An Approach Based on Minimization of the Errors Functions

The main purpose of this paper is to build a new approach for solving a fuzzy linear multi-criterion problem by defining a function called “error function”. For this end, the concept of level set is used to construct the error function. In addition, we introduce the concept of deviation variable in the definition of the error function. The algorithm of the new approach is summarized in three main steps: first, we transform the original fuzzy problem into a deterministic one by choosing a specific level . second, we solve separately each uni-criteria problem and we compute the error function for each criteria. Finally, we minimize the sum of error functions in order to obtain the desired compromise solution. A numerical example is done for a comparative study with some existing approaches to show the effectiveness of the new approach.

New Approaches to the Prognosis and Diagnosis of Breast Cancer Using Fuzzy Expert Systems

Breast cancer remains a significant global health challenge, necessitating effective early detection and prognosis to enhance patient outcomes. Current diagnostic methods, including mammography and MRI, suffer from limitations such as uncertainty and imprecise data, leading to late-stage diagnoses. To address this, various expert systems have been developed, but many rely on type-1 fuzzy logic and lack mobile-based applications for data collection and feedback to healthcare practitioners. This research investigates the development of an Enhanced Mobile-based Fuzzy Expert system (EMFES) for breast cancer pre-growth prognosis. The study explores the use of type-2 fuzzy logic to enhance accuracy and model uncertainty effectively. Additionally, it evaluates the advantages of employing the python programming language over java for implementation and considers specific risk factors for data collection. The research aims to dynamically generate fuzzy rules, adapting to evolving breast cancer research and patient data. Key research questions focus on the comparative effectiveness of type-2 fuzzy logic, the handling of uncertainty and imprecise data, the integration of mobile-based features, the choice of programming language, and the creation of dynamic fuzzy rules. Furthermore, the study examines the differences between the Mamdani Inference System and the Sugeno Fuzzy Inference method and explores challenges and opportunities in deploying the EMFES on mobile devices. The research identifies a critical gap in existing breast cancer diagnostic systems, emphasizing the need for a comprehensive, mobile-enabled, and adaptable solution by developing an EMFES that leverages Type-2 fuzzy logic, the Sugeno Inference Algorithm, Python Programming, and dynamic fuzzy rule generation. This study seeks to enhance early breast cancer detection and ultimately reduce breast cancer-related mortality.

Fuzzy Set-Membership Filtering for Discrete-Time Nonlinear Systems

In this article,the problem of state estimation is addressed for discrete-time nonlinear systems subject to additive unknown-but-bounded noises by using fuzzy set-membership filtering.First,an improved T-S fuzzy model is introduced to achieve highly accurate approximation via an affine model under each fuzzy rule.Then,compared to traditional prediction-based ones,two types of fuzzy set-membership filters are proposed to effectively improve filtering performance,where the structure of both filters consists of two parts:prediction and filtering.Under the locally Lipschitz continuous condition of membership functions,unknown membership values in the estimation error system can be treated as multiplicative noises with respect to the estimation error.Real-time recursive algorithms are given to find the minimal ellipsoid containing the true state.Finally,the proposed optimization approaches are validated via numerical simulations of a one-dimensional and a three-dimensional discrete-time nonlinear systems.

Rough function model and rough membership function

Two pairs of approximation operators, which are the scale lower and upper approximations as well as the real line lower and upper approximations, are defined. Their properties and antithesis characteristics are analyzed. The rough function model is generalized based on rough set theory, and the scheme of rough function theory is made more distinct and complete. Therefore, the transformation of the real function analysis from real line to scale is achieved. A series of basic concepts in rough function model including rough numbers, rough intervals, and rough membership functions are defined in the new scheme of the rough function model. Operating properties of rough intervals similar to rough sets are obtained. The relationship of rough inclusion and rough equality of rough intervals is defined by two kinds of tools, known as the lower （upper） approximation operator in real numbers domain and rough membership functions. Their relative properties are analyzed and proved strictly, which provides necessary theoretical foundation and technical support for the further discussion of properties and practical application of the rough function model.

Generating Type 2 Trapezoidal Fuzzy Membership Function Using Genetic Tuning

Fuzzy inference system(FIS)is a process of fuzzy logic reasoning to produce the output based on fuzzified inputs.The system starts with identifying input from data,applying the fuzziness to input using membership functions(MF),generating fuzzy rules for the fuzzy sets and obtaining the output.There are several types of input MFs which can be introduced in FIS,commonly chosen based on the type of real data,sensitivity of certain rule implied and computational limits.This paper focuses on the construction of interval type 2(IT2)trapezoidal shape MF from fuzzy C Means(FCM)that is used for fuzzification process of mamdani FIS.In the process,upper MF(UMF)and lower MF(LMF)of the MF need to be identified to get the range of the footprint of uncertainty(FOU).This paper proposes Genetic tuning process,which is a part of genetic algorithm(GA),to adjust parameters in order to improve the behavior of existing system,especially to enhance the accuracy of the system model.This novel process is a hybrid approach which produces Genetic Fuzzy System(GFS)that helps to enhance fuzzy classification problems and performance.The approach provides a new method for the construction and tuning process of the IT2 MF,based on the FCM outcomes.The result is compared to Gaussian shape IT2 MF and trapezoid IT2 MF generated by the classic GA method.It is shown that the proposed approach is able to outperform the mentioned benchmarked approaches.The work implies a wider range of IT2 MF types,constructed based on FCM outcomes,and an optimum generation of the FOU so that it can be implemented in practical applications such as prediction,analytics and rule-based solutions.

Optimization of Membership Function for Fuzzy Control Based on Genetic Algorithm and Its Applications

In this paper, a simple and practicable algorithm for optimization of membership function (MF) is proposed. As it is known that MF is very important in the fuzzy control. Unfortunately, to find, especially to optimize MF is always rather complex even difficult. So, to study and develop an effectual aglorithm for MF optimization is a good topic. Allow for the inner advantages of genetic algorithm (GA), it is adopted in the algorithm .The principle and executive procdeure are first presented. Then it is applied in the fuzzy control system of a typical plant. Results of real time run show that the control strategy is encouraging, and the developed algorithm is practicable.

Describing Fuzzy Membership Function and Detecting the Outlier by Using Five Number Summary of Data

One of the most important activities in data science is defining a membership function in fuzzy system. Although there are few ways to describe membership function like artificial neural networks, genetic algorithms etc.;they are very complex and time consuming. On the other hand, the presence of outlier in a data set produces deceptive results in the modeling. So it is important to detect and eliminate them to prevent their negative effect on the modeling. This paper describes a new and simple way of constructing fuzzy membership function by using five-number summary of a data set. Five states membership function can be created in this new method. At the same time, if there is any outlier in the data set, it can be detected with the help of this method. Usually box plot is used to identify the outliers of a data set. So along with the new approach, the box plot has also been drawn so that it is understood that the results obtained in the new method are accurate. Several real life examples and their analysis have been discussed with graph to demonstrate the potential of the proposed method. The results obtained show that the proposed method has given good results. In the case of outlier, the proposed method and the box plot method have shown similar results. Primary advantage of this new procedure is that it is not as expensive as neural networks, and genetic algorithms.

Asymptotically Necessary and Sufficient Quadratic Stability Conditions of T-S Fuzzy Systems Using Staircase Membership Function and Basic Inequality

Asymptotically necessary and sufficient quadratic stability conditions of Takagi-Sugeno (T-S) fuzzy systems are obtained by utilizing staircase membership functions and a basic inequality. The information of the membership functions is incorporated in the stability analysis by approximating the original continuous membership functions with staircase membership functions. The stability of the T-S fuzzy systems was investigated based on a quadratic Lyapunov function. The asymptotically necessary and sufficient stability conditions in terms of linear matrix inequalities were derived using a basic inequality. A fuzzy controller was also designed based on the stability results. The derivation process of the stability results is straightforward and easy to understand. Case studies confirmed the validity of the obtained stability results.

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 reduceMAPE 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.

Fuzz-classification(p,l)-Angel:An enhanced hybrid artificial intelligence based fuzzy logic for multiple sensitive attributes against privacy breaches

The inability of traditional privacy-preserving models to protect multiple datasets based on sensitive attributes has prompted researchers to propose models such as SLOMS,SLAMSA,(p,k)-Angelization,and(p,l)-Angelization,but these were found to be insufficient in terms of robust privacy and performance.(p,l)-Angelization was successful against different privacy disclosures,but it was not efficient.To the best of our knowledge,no robust privacy model based on fuzzy logic has been proposed to protect the privacy of sensitive attributes with multiple records.In this paper,we suggest an improved version of(p,l)-Angelization based on a hybrid AI approach and privacy-preserving approach like Generalization.Fuzz-classification(p,l)-Angel uses artificial intelligence based fuzzy logic for classification,a high-dimensional segmentation technique for segmenting quasi-identifiers and multiple sensitive attributes.We demonstrate the feasibility of the proposed solution by modelling and analyzing privacy violations using High-Level Petri Nets.The results of the experiment demonstrate that the proposed approach produces better results in terms of efficiency and utility.

Fuzzy System Design Using Current Amplifier for 20 nm CMOS Technology

In the recent decade,different researchers have performed hardware implementation for different applications covering various areas of experts.In this research paper,a novel analog design and implementation of different steps of fuzzy systems with current differencing buffered amplifier(CDBA)are proposed with a compact structure that can be used in many signal processing applications.The proposed circuits are capable of wide input current range,simple structure,and are highly linear.Different electrical parameters were compared for the proposed fuzzy system when using different membership functions.The novelty of this paper lies in the electronic implementation of different steps for realizing a fuzzy system using current amplifiers.When the power supply voltage of CDBA is 2V,it results in 155mW,power dissipation;4.615KΩ,input resistance;366KΩ,output resistances;and 189.09 dB,common-mode rejection ratio.A 155.519 dB,voltage gain,and 0.76V/μs,the slew rate is analyzed when the power supply voltage of CDBAis 3V.The fuzzy system is realized in 20nm CMOS technology and investigated with an output signal of high precision and high speed,illustrating that it is suitable for realtime applications.In this research paper,a consequence of feedback resistance on the adder circuit and the defuzzified circuit is also analyzed and the best results are obtained using 100K resistance.The structure has a low hardware complexity leading to a low delay and a rather high quality.

Improved Bacterial Foraging Optimization Algorithm Based on Fuzzy Control Rule Base

Manual construction of a rule base for a fuzzy system is the hard and time-consuming task that requires expert knowledge.In this paper we proposed a method based on improved bacterial foraging optimization（IBFO）,which simulates the foraging behavior of “E.coli” bacterium,to tune the Gaussian membership functions parameters of an improved Takagi-Sugeno-Kang fuzzy system（C-ITSKFS） rule base.To remove the defect of the low rate of convergence and prematurity,three modifications were produced to the standard bacterial foraging optimization（BFO）.As for the low accuracy of finding out all optimal solutions with multi-method functions,the IBFO was performed.In order to demonstrate the performance of the proposed IBFO,multiple comparisons were made among the BFO,particle swarm optimization（PSO）,and IBFO by MATLAB simulation.The simulation results show that the IBFO has a superior performance.

Optimum path planning of mobile robot in unknown static and dynamic environments using Fuzzy-Wind Driven Optimization algorithm

This article introduces a singleton type-1 fuzzy logic system(T1-SFLS) controller and Fuzzy-WDO hybrid for the autonomous mobile robot navigation and collision avoidance in an unknown static and dynamic environment. The WDO(Wind Driven Optimization) algorithm is used to optimize and tune the input/output membership function parameters of the fuzzy controller. The WDO algorithm is working based on the atmospheric motion of infinitesimal small air parcels navigates over an N-dimensional search domain. The performance of this proposed technique has compared through many computer simulations and real-time experiments by using Khepera-Ⅲ mobile robot. As compared to the T1-SFLS controller the Fuzzy-WDO algorithm is found good agreement for mobile robot navigation.

Automated Detection of Contaminated Radar Image Pixels in Mountain Areas

In mountain areas, radar observations are often contaminated （1） by echoes from high-speed moving vehicles and （2） by point-wise ground clutter under either normal propagation （NP） or anomalous propagation （AP） conditions. Level Ⅱ data are collected from KMTX （Salt Lake City, Utah） radar to analyze these two types of contamination in the mountain area around the Great Salt Lake. Human experts provide the ＂ground truth＂ for possible contamination of either type on each individual pixel. Common features are then extracted for contaminated pixels of each type. For example, pixels contaminated by echoes from high-speed moving vehicles are characterized by large radial velocity and spectrum width. Echoes from a moving train tend to have larger velocity and reflectivity but smaller spectrum width than those from moving vehicles on highways. These contaminated pixels are only seen in areas of large terrain gradient （in the radial direction along the radar beam）. The same is true for the second type of contamination - pointwise ground clutters. Six quality control （QC） parameters are selected to quantify the extracted features. Histograms are computed for each QC parameter and grouped for contaminated pixels of each type and also for non-contaminated pixels. Based on the computed histograms, a fuzzy logical algorithm is developed for automated detection of contaminated pixels. The algorithm is tested with KMTX radar data under different （clear and rainy） weather conditions.

Gyroscope Fault Diagnosis Using Fuzzy SVM to Unbalanced Samples

A novel fuzzy support vector machine based on unbalanced samples(FSVM-US)is proposed to solve the high false positive rate problem since the gyroscope output is susceptible to unmanned aerial vehicle(UAV)airborne electromagnetic environment and the gyroscope abnormal signal sample is rather rare.Firstly,the standard deviation of samples projection to normal vector for SVM classifier hyper plane is analyzed.The imbalance feature expression reflecting the hyper plane shift for the number imbalance between samples and the dispersion imbalance within samples is derived.At the same time,the denoising factor is designed as the exponential decay function based on the Euclidean distance between each sample and the class center.Secondly,the imbalance feature expression and denoising factor are configured into the membership function.Each sample has its own weight denoted the importance to the classifier.Finally,the classification simulation experiments on the gyroscope fault diagnosis system are conducted and FSVM-US is compared with the standard SVM,FSVM,and the four typical class imbalance learning(CIL)methods.The results show that FSVM-US classifier accuracy is 12% higher than that of the standard SVM.Generally,FSVM-US is superior to the four CIL methods in total performance.Moreover,the FSVMUS noise tolerance is also 17% higher than that of the standard SVM.

Screening and evaluation of reliable traits of upland cotton(Gossypium hirsutum L.)genotypes for salt tolerance at the seedling growth stage

Background:Salt stress significantly inhibits the growth,development,and productivity of cotton because of osmotic,ionic,and oxidative stresses.Therefore,the screening and development of salt tolerant cotton cultivars is a key issue towards sustainable agriculture.This study subjected 11 upland cotton genotypes at the seedling growth stage to five different salt concentrations and evaluated their salt tolerance and reliable traits.Results:Several morpho-physiological traits were measured after 10 days of salinity treatment and the salt tolerance performance varied significantly among the tested cotton genotypes.The optimal Na Cl concentration for the evaluation of salt tolerance was 200 mmol·L-1.Membership function value and salt tolerance index were used to identify the most consistent salt tolerance traits.Leaf relative water content and photosynthesis were identified as reliable indicators for salt tolerance at the seedling stage.All considered traits related to salt tolerance indices were significantly and positively correlated with each other except for malondialdehyde.Cluster heat map analysis based on the morpho-physiological salt tolerance-indices clearly discriminated the 11 cotton genotypes into three different salt tolerance clusters.Cluster I represented the salt-tolerant genotypes(Z9807,Z0228,and Z7526)whereas clusters II(Z0710,Z7514,Z1910,and Z7516)and III(Z0102,Z7780,Z9648,and Z9612)represented moderately salttolerant and salt-sensitive genotypes,respectively.Conclusions:A hydroponic screening system was established.Leaf relative water content and photosynthesis were identified as two reliable traits that adequately represented the salt tolerance of cotton genotypes at the seedling growth stage.Furthermore,three salt-tolerant genotypes were identified,which might be used as genetic resources for the salt-tolerance breeding of cotton.