Membership Function Generator Research Articles

Application of a new fuzzy logic model known as "SMRGT" for estimating flow coefficient rate

Since we all have our own set of limitations when it comes to perceiving the world and reasoning profoundly, we are constantly met with uncertainty as a result of a lack of information (lexical impression, incompleteness), as well as specific measurement inaccuracies. It has been found that uncertainty, which shows up as ambiguity, is the root cause of complexity, which is everywhere in the real world. Most of the uncertainty in civil engineering systems comes from the fact that the constraints (parameters) are hard to understand and are described in a vague way. The ambiguity comes from a number of sources, including physical arbitrariness, statistical uncertainty due to using limited information to estimate these characteristics, and model uncertainty due to using overly simplified methods and idealized depictions of actual performances. Thus, it is better to combine fuzzy set theory and fuzzy logic. Fuzzy logic is well-suited to modelling the indeterminacy and ambiguity that results from multiple factors and a lack of data. In order to improve upon a previous predictive model, this paper uses a smart model built on a fuzzy logic system (FLS). Precipitation, temperature, humidity, slope, and land use data were all taken into account as input variables in the fuzzy model. Toprak's original explanation of the simple membership function and fuzzy rules generation technique (SMRGT) was based on the fuzzy-Mamdani methodology and used the flow coefficient as its output. The model's results were compared to available data. The following factors were considered in the comparison: 1) The maximum, minimum, mean, standard deviation, skewness, variation, and correlation coefficients are the seven statistical parameters. 2) Four types of error criteria: Mean Absolute Relative Error (MARE), Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). 3) Scatter diagram.

Digital Filtering Techniques Using Fuzzy-Rules Based Logic Control.

This paper discusses current formulations based on fuzzy-logic control concepts as applied to the removal of impulsive noise from digital images. We also discuss the various principles related to fuzzy-ruled based logic control techniques, aiming at preserving edges and digital image details efficiently. Detailed descriptions of a number of formulations for recently developed fuzzy-rule logic controlled filters are provided, highlighting the merit of each filter. Fuzzy-rule based filtering algorithms may be designed assuming the tailoring of specific functional sub-modules: (a) logical controlled variable selection, (b) the consideration of different methods for the generation of fuzzy rules and membership functions, (c) the integration of the logical rules for detecting and filtering impulse noise from digital images. More specifically, we discuss impulse noise models and window-based filtering using fuzzy inference based on vector directional filters as associated with the filtering of RGB color images and then explain how fuzzy vector fields can be generated using standard operations on fuzzy sets taking into consideration fixed or random valued impulse noise and fuzzy vector partitioning. We also discuss how fuzzy cellular automata may be used for noise removal by adopting a Moore neighbourhood architecture. We also explain the potential merits of adopting a fuzzy rule based deep learning ensemble classifier which is composed of a convolutional neural network (CNN), a recurrent neural networks (RNN), a long short term memory neural network (LSTM) and a gated recurrent unit (GRU) approaches, all within a fuzzy min-max (FMM) ensemble. Fuzzy non-local mean filter approaches are also considered. A comparison of various performance metrics for conventional and fuzzy logic based filters as well as deep learning filters is provided. The algorhitms discussed have the following advantageous properties: high quality of edge preservation, high quality of spatial noise suppression capability especially for complex images, sound properties of noise removal (in cases when both mixed additive and impulse noise are present), and very fast computational implementation.

Flow Coefficient Determination in Catchment Based on Analysis of Temperature and Wind Speed Data Using the Fuzzy SMRGT Method

In engineering hydrology, calculating the flow coefficient is a crucial step. The flow coefficient calculation is necessary for directing the rational profiteering of water resources, improving the overall efficiency of water resource utilization, and minimizing the effect of catastrophic events. By precisely determining the flow coefficient, which is the most influential factor in flood flow, the current issues will be mitigated substantially. Various techniques are available in the existing literature for modelling flow coefficient. Most of them, however, rely on black-box approaches that are not generalizable. Therefore, this paper applied an intelligent model based on a fuzzy logic system called the Simple Membership Function and Fuzzy Rules Generation Technique (SMRGT). The new technique considers the physical cause-effect relationship and is intended to aid individuals who struggle to choose the number, form, and logic of membership functions and fuzzy rules in any fuzzy set. The study area’s temperature and wind speed data were incorporated into the SMRGT model’s input variables. The output was the flow coefficient. The prediction made by the model was validated against observational data. The comparison relies on numerous statistics and errors. The results indicated that the SMRGT model predicts the flow coefficient extraordinarily well and is an excellent method for generating membership functions and fuzzy rules.

Automatic interval management for aircraft based on dynamic fuzzy speed control considering uncertainty

A novel real-time autonomous Interval Management System (IMS) is proposed to automate interval management, which considers the effect of wind uncertainty using the Dynamic Fuzzy Velocity Decision (DFVD) algorithm. The membership function can be generated dynamically based on the True Air Speed (TAS) limitation changes in real time and the interval criterion of the adjacent aircraft, and combined with human cognition to formulate fuzzy rules for speed adjusting decision-making. Three groups of experiments were conducted during the en-route descent stage to validate the proposed IMS and DFVD performances, and to analyze the impact factors of the algorithm. The verification experimental results show that compared with actual flight status data under controllers’ command, the IMS reduces the descent time by approaching 30% with favorable wind uncertainty suppression performance. Sensitivity analysis shows that the ability improvement of DFVD is mainly affected by the boundary value of the membership function. Additionally, the dynamic generation of the velocity membership function has greater advantages than the static method in terms of safety and stability. Through the analysis of influencing factors, we found that the interval criterion and aircraft category have no significant effect on the capability of IMS. In a higher initial altitude scenario, the initial interval should be appropriately increased to enhance safety and efficiency during the descent process. This prototype system could evolve into a real-time Flight-deck Interval Management (FIM) tool in the future.

A Comparative Study of Using Adaptive Neural Fuzzy Inference System (ANFIS), Gaussian Process Regression (GPR), and SMRGT Models in Flow Coefficient Estimation

Estimating the flow coefficient is a crucial hydrologic process that plays a significant role in flood forecasting, water resource planning, and flood control. Accurate prediction of the flow coefficient is essential to prevent flood-related losses, manage flood warning systems, and control water flow. This study aimed to predict the flow coefficient for a period of 19 years (2000-2019) in the Aksu River Sub-Basin in Turkey, using historical climatic data, including precipitation, temperature, and humidity, provided by The Turkish State of Meteorological Service (TSMS). The study utilized three different approaches, namely, the Adaptive Neural Fuzzy Inference System (ANFIS), Simple Membership function and fuzzy Rules Generation Technique (SMRGT), and Gaussian Process Regression (GPR), to predict the flow coefficient. The models were evaluated using several statistical tests, such as Root Mean Square Error (RMSE), Coefficient of Determination (R2), Mean Absolute Error (MAE), and Mean Square Error (MSE), to determine their accuracy. Based on the evaluation criteria, it is concluded that the Simple Membership Functions and Fuzzy Rules Generation Technique (SMRGT) model has superior flow coefficient estimation performance than the other models.

Ultra‐efficient fully programmable membership function generator based on independent double‐gate FinFET technology

SummaryThis paper demonstrates an ultra‐efficient, fully programmable membership function generator (MFG) utilizing independent double‐gate (IDG) FinFET technology. The proposed MFG can produce s‐shaped, z‐shaped, triangular, and trapezoidal membership functions. By employing only six transistors, the designed MFG provides full controllability over the height, position, width, and slope of the generated waveforms. Without using any additional transistor and changing the dimensions, the proposed MFG can calibrate the slope of the output based on the back‐gate bias voltage of the IDG‐FinFETs. According to our extensive simulations, the proposed MFG shows promising improvements in transistor count (70%), power‐delay‐product (PDP) (80%), and maximum absolute error (61%) as compared with its state‐of‐the‐art counterparts. To benchmark the functionality of the proposed MFG in practical applications, our generated membership function is exploited as the neuron's activation function in a multilayer perceptron (MLP) neural network. The simulation results indicate that the training process of the simulated MLP with the proposed MFG closely tracks the results obtained from the ideal MLP with the sigmoid activation function. A figure of merit (FoM) is defined considering the hardware efficiency and accuracy of the neural networks to evaluate the entire performance of the proposed MFG. The FoM simulations demonstrate that the proposed MFG presents an excellent trade‐off between hardware performance and accuracy in neural network applications. Our results emphasize that the proposed MFG is a potential candidate for designing high‐performance on‐chip neural networks.

A high-performance and ultra-efficient fully programmable fuzzy membership function generator using FinFET technology for image enhancement

In fuzzy systems, efficient programmable membership function generators (MFGs) are the canonical point for the fuzzification process. This work demonstrates a high-performance programmable MFG using 7 nm FinFET technology. The proposed MFG can generate S-shaped, Z-shaped, Gaussian-shaped, and Generalized Bell-shaped membership functions. The proposed design employs 14 FinFETs to control the produced waveforms’ position, height, width, and slope. According to the simulations, the proposed MFG offers remarkable improvements in transistor count (39%), power-delay product (PDP) (54%), absolute error (45%), and root mean square error (36%) compared to the previous MFGs. The proposed design has been utilized for image enhancement to evaluate the performance of the proposed MFG in realistic environments. The image enhancement simulation results indicate a higher peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM) than previous related works. A figure of merit (FoM) is defined considering the image enhancement quality metrics and circuit efficiency to benchmark the entire performance of the proposed MFG. The FoM simulations demonstrate that the proposed design shows an excellent trade-off between the circuit performance and image enhancement quality. Our results confirm that the proposed MFG is suitable for developing high-performance on-chip image processing applications.

A comparative study of multiband mamdani fuzzy classification methods for west of Iraq satellite image

In our paper, performance of four fuzzy membership function generation methods was studied. These methods were studied in the context of implementing Mamdani fuzzy classification on a set of satellite images for western Iraqi territory. The first method generate triangulate membership functions using mean, minimum (min) and maximum (max) of histogram attribute values (AV), while peak and standard deviation (STD) of these AV were used in the second. On the other hand, in the third and fourth methods, Gaussian membership functions are generated using same mentioned values in the first and second method respectively. The goal was to generate a Mamdani type fuzzy inference system the membership function (MF) of each fuzzy set and implementing the AV of western Iraqi territory training data sets. A pixel-by-pixel comparison of each method with traditional maximum likelihood method (ML) was made on data sets comprising six bands of satellite imagery of the western Iraqi region taken by the Landsat-5 satellite. Simulation results of these performance comparisons singled out that the method using Gaussian MFs together with peak and STD of the AV as the best achiever with a similarity of 83.16 percent for band (3) of the studied area.

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.

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.

Prediction of Sunspots using Fuzzy Logic: A Triangular Membership Function-based Fuzzy C-Means Approach

Fuzzy logic is an algorithm that works on “degree of truth”, instead of the conventional crisp logic where the possible answer can be 1 or 0. Fuzzy logic resembles human thinking as it considers all the possible outcomes between 1 and 0 and it tries to reflect reality. Generation of membership functions is the key factor of fuzzy logic. An approach for generating fuzzy gaussian and triangular membership function using fuzzy c-means is considered in this research. The problem related to sunspot prediction is considered and its accuracy is calculated. It is evident from the results that the proposed technique of generating membership functions using fuzzy c-means can be adopted for predicting sunspots.

Design of a Low Power Analog and Multi-shaped Fully Programmable Twin-Cell Membership Function Generator Circuit in 65 nm CMOS Technology

This paper presents a new approach for implementing an analog fully programmable membership function generator (MFG) with twin-cell topology which can operate in Transconductance-mode. The proposed multi-shaped MFG circuit is capable of generating the trapezoidal, triangular, S- and Z-shaped membership functions. Also, a full programmability disposition in all parameters of height, position and width is achievable. To make this purpose, a simple structure of complementary differential pairs is utilized which changing of their applied input constant voltages and bias current led into programming, independently. Moreover, the proposed MFG architecture has a stellar functionality which makes it suitable for implementing as a general-purpose fuzzy logic controller. Since in this proposed MFG approach, only 26 transistors are used, the chip area and total power dissipation can be decreased to 552 µm2 and 83.8 µW, respectively, which is reduced in comparison with the other works. The proposed MFG circuit is simulated in 65 nm standard CMOS technology with a single supply voltage of 1.2 V. The post-layout simulation results show that the proposed twin-cell MFG circuit is in great collusion which indicates a high delicacy of the proposed design, and the circuit presents the profit of negligible linearity error less than 5.2%. Furthermore, this proposed circuit is compared with a simulated MFG circuit named the comparative MFG, and the exported results show the improved functionality of the proposed twin-cell MFG, specifically.

A high-performance fully programmable membership function generator based on 10 nm gate-all-around CNTFETs

This paper presents a novel fully programmable membership function generator (MFG) based on carbon nanotube field effect transistors (CNTFETs). The proposed CNTFET-based MFG has full control over the output characteristics such as height, and position independently. The proposed design can produce four different membership functions only by calibrating the external control bias voltages. Moreover, two peripheral transmission gates are utilized to provide controllability over the slope of the generated membership functions in a wide range. The primary advantage of the proposed MFG compared to its counterparts is the ability to control the slope of the resulted membership functions entirely and separately using only two external bias voltages without the need for changing the device physical dimensions. The simulation results demonstrate that the proposed design improves the power, delay, and layout area, on average by, 72%, 55%, and 33% as compared to the previous designs. Furthermore, the proposed MFG shows only 2.13% RMS error, and 5.65% maximum absolute error for the generated Gaussian trapezoidal membership function as compared to the ideal form. Our results certify that the proposed CNTFET-based MFG has the potential to be used as a promising platform for fuzzy inference systems in deep nanoscale technology nodes.

Energy efficient multi-objective scheduling of tasks with interval type-2 fuzzy timing constraints in an Industry 4.0 ecosystem

Industrial systems usually draw huge energy to run various machines. The amount of energy requirement has again increased due to the automation of the industrial plants to make them Industry 4.0 compliant. As a result, demand of energy is on the rise in almost all manufacturing and industrial plants. The necessity of critical and smart manufacturing processes in Industry 4.0 and its increased energy requirements force us to look for energy efficient techniques for running the deployed computing systems, which are often embedded and integrated within larger machines and have to function under time constraints. Computational efficiency of these real-time embedded systems (RTESs) depends solely on the timely completion of tasks. Task execution with less energy consumption within critical timing constraints is a challenging issue for the designers of RTESs. Thus, task scheduling in these systems require sophisticated energy efficient mechanisms. However, energy efficiency and timeliness are two mutually contradictory objectives, since the former is achieved only with a significant compromise of the later. In this paper, we propose a novel approach, based on the popular multi-objective evolutionary algorithm, Non-dominated sorting genetic algorithm-II, to solve this problem. Moreover, in RTESs, precise prediction of timing constraints is difficult before runtime which causes a form of imprecision or uncertainty in the system. Therefore, we use type-2 fuzzy sets (T2 FSs) to model the timing constraints in RTESs and introduce novel algorithms for membership function generation and calculation of fuzzy earliness. Numerical as well as real-life examples are included to demonstrate our proposed technique.

Detecting deviations from activities of daily living routines using kinect depth maps and power consumption data

There has been an increase in the world’s population of elderly persons who wish to live independently for as long as possible. This paper presents an unsupervised approach to help caregivers detect deviations from daily routines of elderly people living alone via inexpensive minimally intrusive sensors. The approach employs a power sensor to measure household composite power consumption and a small number of Kinect sensors in functional areas of the house to capture depth maps from the occupant’s activities of daily living (ADLs). The ADLs in an unlabelled training dataset are identified based on associating the occupant’s locations with specific power signatures on the power line. This training dataset is processed in order to model key features of ADLs, including the regularity and frequency of important activities. The approach uses a novel data-driven technique to define fuzzy sets over ADL attributes tailored to the occupant’s behaviour patterns. The membership functions of these fuzzy sets are learned based on the data distribution of attributes. A set of fuzzy rules is generated to indicate the occupant’s deviation from the normal routine of ADLs in subsequent data. The outputs of this monitoring system are reports on upward and downward deviations from normal behaviour patterns in the form of both numerical and linguistic information. The assessment of these scores over a long-term can help caregivers detect the warning signs of persistent drifts from the daily routine. As a proof of concept, the proposed monitoring approach was evaluated using two datasets collected from real-life settings. The fuzzy rule set obtained from the output of the proposed membership function generation technique was able to effectively monitor the ADLs of elderly people because it could accurately distinguish periods of deviations from the routine performance of ADLs. Compared to existing monitoring techniques, the proposed method required no prior information about the appliances in use and its output was considered to be more helpful for caregivers. The fuzzy inference system in this approach was found to be robust in regard to errors when identifying ADLs as it could effectively classify normal and abnormal behaviour patterns of the occupant despite errors in the list of the used appliances.

A fuzzy Anti-lock braking system (ABS) controller using CMOS circuits

In this paper, an Anti-lock braking system (ABS) is designed using a fuzzy logic controller (FLC). The FLC is designed in 0.35 μm standard CMOS process and this design of the circuit makes it having high speed and low power consumption. Also, all the controlling signals and input signals are in voltage form and there is no need for digital programming. Voltage mode design of the circuits leads to the convenient use of FLC with sensors. The objective function is designed to keep the wheel slip to a desired level so that maximum wheel tractive force and maximum vehicle deceleration are achieved. By training this function to the Adaptive Nero-Fuzzy Inference System (ANFIS) of MATLAB software the main parameters of fuzzy controller singletons and shape of Membership Function Generators (MFGs) are measured, then all the circuits of the proposed fuzzy logic controller is designed using Complementary Metal-Oxide-Semiconductor (CMOS) circuits, at the end simulation results of the fuzzy logic controller using Hspice simulator and BSIM3v3 (Berkeley Short-Channel Insulated gate field effect transistor Model) parameters verifies the performance and functionality of the design.

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.

FAME-ADL: a data-driven fuzzy approach for monitoring the ADLs of elderly people using Kinect depth maps

This paper presents a novel unsupervised fuzzy approach to monitoring the activities of daily living (ADLs) of elderly people living alone. Kinect sensors are employed in functional areas of the monitored environment to capture unlabelled training depth maps from the occupant’s ADLs. This training dataset is processed in order to extract a set of ADL attributes, including the occupant’s location and 3D posture. A novel data-driven technique is presented to define fuzzy sets over ADL attributes, and membership functions are learned based on the attribute data distribution. These fuzzy sets are used to transform the attributes’ values into fuzzy labels in order to linguistically describe the occupant’s behaviour patterns. A set of fuzzy rules is generated to model the frequency of behaviour patterns using a novel data-driven technique. Abnormal behaviours in subsequent data are detected as unusual behaviours that last for longer than a learned duration. The proposed monitoring approach was evaluated using a dataset collected from a real-life setting. The fuzzy rule set obtained from the output of the proposed membership function generation technique was found to improve monitoring of the elderly because it could accurately classify more testing scenarios of normal and abnormal behaviours when compared to the rule sets obtained using other techniques. Compared to the output of other parameterising techniques, this fuzzy rule set had fewer rules and greater tolerance of fine variations in activities. The experimental results also showed that the adopted fuzzy approach for monitoring ADLs outperformed existing camera-based approaches in terms of its classification accuracy for testing scenarios of normal and abnormal behaviours.

Design of a memristor based fuzzy processor

This paper presents a mixed-signal hybrid CMOS/Nano circuit to implement a fully programmable fuzzy processor that performs the zero-order Sugeno's algorithm. The programmability is incorporated in both the rule base and the membership function generator. Membership functions are stored in a memristor-based crossbar array, whereas digital memory is utilized to form a rule base. Each linguistic value whose shape can be independently chosen is stored in each row of the crossbar. Moreover, the adjacent linguistic values can have any order of overlapping ratio. Consequently, a very powerful and flexible fuzzifier is designed.The analog signal processing blocks are designed in both current and voltage mode to simplify the circuit complexity while the digital circuitry is utilized to add programmability to the whole processor. A fuzzy logic controller with nine rules, two inputs, and one output was successfully simulated using HSPICE with 350 nm technology. Simulation results confirm the proper operation of the processor that can operate up to 10 MFLIPS (Mega Fuzzy Logic Inference Per Seconds) while consuming 3.49 mW (@vdd 3.3v). Furthermore, simulation results show that an equivalent precision of 6-bits is achieved in the output signal generation. The circuit is laid out and it takes an area of approximately 0.614 mm2.

A low-power, fully programmable membership function generator using both transconductance and current modes

In this paper, a membership function generator (MFG) is presented which can produce triangular, trapezoidal, S-shaped or Z-shaped membership functions. The presented structure can operate in both trans-conductance (voltage–current) and current mode (current–current) with high programmability. The slope (keeping the height or width of the membership function constant), height (keeping the width constant) and width (keeping the height constant) of the membership function are independently programmable. Moreover, the width of the trapezoid can be varied without changing slope or height and all the generated shapes can be positioned horizontally. The topology, primarily, is presented in transconductance mode which can be converted to current mode with a little change in the input section of the circuit. Simulation results for the proposed circuit are presented in two operation modes. The most remarkable feature of the proposed MFG is its capability of generating outputs while consumes a very low amount of power. The analytical analysis of the proposed circuits is also given in HSPICE using TSMC 0.18 μm CMOS technology.