Shape Of Membership Functions Research Articles

Optimal voltage control and loss reduction in microgrid by active and reactive power generation

This study suggests a new algorithm based on a combination of fuzzy logic and genetic algorithm (GA) to improve voltage profile and reduce loss in a microgrid. Considered microgrid includes control variables such as onload tap changer (OLTC), active power output of distributed generators (DG) and reactive power output of feeder switched capacitors that are controlled in a microgrid controller (MGC) through communication links. The proposed method was used to obtain the optimum value of control variables to reduce the loss and improve the voltage profile. The problem formulations consider three distinct objectives related to cost of loss, cost of injected active power by infinity bus and cost of injected active power by DG. The novel formulation is a multi-objective and non-differentiable optimization problem. The control variables were changed base on fuzzy logic and the GA was employed for finding the optimum shape of membership functions. In order to verify the proposed method, a 34 bus microgrid was analyzed in varying load condition and was compared with previous works. Finally it has been tested on 33-buses system to be investigated the control variables status, cost function status and voltage fluctuation during one day.

Modeling the effect of variable work piece hardness on surface roughness in an end milling using multiple regression and adaptive Neuro fuzzy inference system

Article history: Received July 2 2013 Received in revised format September 7 2013 Accepted November 23 2013 Available online November 26 2013 The aim of this study is to correlate work piece material hardness with surface roughness in prediction studies. The proposed model is for prediction of surface roughness of tool steel materials of hardness 55 HRC to 62 HRC (±2 HRC). The machining experiments are performed under various cutting conditions using work piece of different hardness. The surface roughness of these specimens is measured. The result showed that the influence of work piece material hardness on surface finish is significant for cutting speed and feed in CNC end milling operation. It is also observed that the surface roughness prediction accuracy of Adaptive neuro fuzzy inference system using triangular membership function is better than Gaussian, bell shape membership function and regression analysis. Surface roughness prediction accuracy with material hardness as input parameter is 97.61%. © 2014 Growing Science Ltd. All rights reserved

Imperialist competitive algorithm–based adaptive fuzzy control of a pneumatic actuator

This article presents an investigation into the application of a constrained imperialist competitive algorithm with a new penalty function to optimize an adaptive fuzzy proportional–integral–derivative controller for a pneumatic actuator. The integral absolute error and the maximum overshoot of the control system are considered as the cost functions. The constrained imperialist competitive algorithm–based optimization scheme is thus conducted to obtain the best structure of the fuzzy proportional–integral–derivative controller involving optimum shape and location of membership functions and suitable value of scale factors. Then, a simulation study based on the identified model of the pneumatic actuator and three control approaches namely conventional proportional–integral–derivative control, genetic algorithm–based adaptive fuzzy proportional–integral–derivative control and the proposed constrained imperialist competitive algorithm–based adaptive fuzzy proportional–integral–derivative control is carried out to evaluate the performance of the proposed controllers. Finally, an experimental rig is developed to verify the simulation outcomes. It is found that the constrained imperialist competitive algorithm–based fuzzy controller converges faster to an optimum solution compared to the genetic algorithm method. Besides, the superiority of the proposed constrained imperialist competitive algorithm–based design over other controllers is demonstrated.

A method based on shape-similarity for detecting similar opinions in group decision-making

In this paper, we propose a method to identify groups of similarly shaped membership functions representing criterion preferences provided by a large group of experts in the context of group decision-making. Our hypothesis hereby is that similarly shaped membership functions reflect similar expert opinions. The proposed method uses a symbolic notation to depict each membership function taking into account its shape characteristics (i.e., slopes and preference levels) and the relative length approximations on its X-axis segments (i.e., core segments, left and right spreads). The symbolic notation significantly reduces the complexity to handle a large group of expert opinions expressed by membership functions, and facilitates their comparison for grouping purposes through a shape-similarity measure.The main goal of the method is to detect all membership functions that are relevant to represent trends or suitable concepts among a large group of people considered as experts. An illustrative example, demonstrating the applicability of the method, is included in the paper.

The Half-Cycle Correction Revisited

Decision-analytic software commonly used to implement discrete Markov models requires transitions to occur between simulated health states either at the beginning or at the end of each cycle. The result is an over- or underestimation, respectively, of quality-adjusted life expectancy and cost, compared with the results that would be obtained if transitions were modeled to occur randomly throughout each cycle. The standard half-cycle correction (HCC) is used to remedy the bias. However, the standard approach to the HCC is problematic: It does not account for discounting or for the shape of intermediate state membership functions. Application of the standard approach to the HCC also has no numerical effect on the resulting incremental cost-effectiveness ratio or change in net health benefit under certain circumstances. Alternatives to the standard HCC, in order of ease of use, include no correction, the life-table approach, the cycle-tree method, and a correction based on Simpson's rule. For less complex decision models in which the computational burden is not large, reducing the cycle length to a month or less and using no correction should result in small estimation biases. With more complex models, where cycle lengths larger than 1 month may be necessary to make computation feasible, we recommend the cycle tree approach. The latter is relatively easy to apply and has an intuitive appeal: Hypothetical subjects who transition from one state to another, on average halfway through a cycle, should receive half of the value associated with the state from which they come and half the value of the state to which they are going.

A novel maritime risk assessment model incorporating a fuzzy rule‐based approach

AbstractBecause of the lack or incompleteness of data, a number of maritime risk studies based on fuzzy rule‐based approaches have been proposed. However, two drawbacks can be identified in such approaches. The first is the low degree of discrimination of risk results because of the membership function distribution of the linguistic terms describing inputs. The lack of discrimination becomes degenerative in circumstances where the membership functions of linguistic terms are disproportionately distributed. The second is the lack of consideration for different effects of risk results caused by input weights because of the loss of information from the inputs. By developing a new mechanism for constructing rules and fuzzy conclusions, a risk modelling capable of overcoming such difficulties is proposed. The methodology is validated using two case studies selected from the literature. It is concluded that the framework is able to provide better discrimination of risk results. Risk outcomes can be generated with confidence regardless of the membership function shapes of linguistic terms in both the antecedent and consequent parts. It also has the feature that enables risk results to be sensitive to the minor alterations of input weights.

ANFIS-based approach to studying subthreshold behavior including the traps effect for nanoscale thin-film DG MOSFETs

A fuzzy framework based on an adaptive network fuzzy inference system (ANFIS) is proposed to evaluate the relative degradation of the basic subthreshold parameters due to hot-carrier effects for nanoscale thin-film double-gate (DG) MOSFETs. The effect of the channel length and thickness on the resulting degradation is addressed, and 2-D numerical simulations are used for the elaboration of the training database. Several membership function shapes are developed, and the best one in terms of accuracy is selected. The predicted results agree well with the 2-D numerical simulations and can be efficiently used to investigate the impact of the interface fixed charges and quantum confinement on nanoscale DG MOSFET subthreshold behavior. Therefore, the proposed ANFIS-based approach offers a simple and accurate technique to study nanoscale devices, including the hot-carrier and quantum effects.

Application of Fuzzy Set Theory and SPOT Satellite Images in Site Selection of Public Libraries (Case Study: Saqqez City, Iran)

Easy availability to public libraries is one of the main keys in successful presentation of public library services with high quality will be valueless for those have not access to them. Availability to services should be configured in such a way that maximizes easiness and facility for potential and de facto users. In present research the required number of public libraries and establishment situation of public libraries in Saqqez city in Kurdistan province in North West of Iran were investigated and evaluated by using fuzzy set theory and SPOT 5 satellite images in 2009. In addition, optimum regions for generation of new public libraries were distinguished in city area in form of recommended map. Results showed that weighting variables classes were determined between 0-1 (profusion proportion). Fuzzification each one of effective factors using IDRISI software terminated and after was assigned type and shape membership function. Final map of suitable regions for construction of new public libraries in saqqez city area indicates that none of the libraries were situated in suitable regions.

Self Learning Intelligent Controller of Electro hydraulic Actuator

The use of electro-hydraulic cylinders in industry has been becoming increasingly popular. A Fu zzy Logic Controller (FLC), which consists of the linguistic defined fuzzy sets and the control rules, is designed to control of the electro-hydraulic cylinder. Using the triangle shaped membership function; the position of the servo cylinder was successfully controlled. When the system working condition is altered, the control algorith m is shown to be more robust compared to the PID controller.

Broad area mapping of monthly soil erosion risk using fuzzy decision tree approach: integration of multi-source data within GIS

Soil erosion poses a serious problem for sustainable agriculture and the environment. There is a need to develop a simple and practical approach for broad area mapping of soil erosion risk that uses the uncertain but available information as input data within the constraints of reasonable cost and time. In this work, a predictive approach for conducting analytical erosion risk assessment across broad areas is developed, which combines a fuzzy decision tree (FDT), remote sensing and Geographic Information System (GIS). This approach is applicable to situations with a limited amount of input data and can easily adjust assessment factors according to actual need. In this study, four dominating factors affecting soil erosion were considered: soil, topography, land cover and climate. GIS thematic layers of these factors were constructed followed by fuzzified analysis through trapezoidal shaped membership functions. Based on subdivided erosion response units (ERUs), an optimal FDT was determined to classify monthly soil erosion risk into five levels. High-risk and very high-risk soil erosion in the study area is mainly concentrated from June to August, with July and August showing the highest risk covering more than 80% of the study area. November to March is dominated by low risk over more than 90% of the study area, while medium risk is dominant in April, May, September and October. Compared with field survey data, the fuzzy decision erosion risk assessment approach was shown to be applicable and economical for rapidly identifying and locating soil erosion risk with limited input data by means of remote sensing and GIS.

Active and reactive power flow regulation for a grid connected vsc based on fuzzy controllers

Voltage Source Converters (VSC) are one of the most used converters in distributed generation application and HVDC systems, since a VSC can operate either as inverter or rectifier. Furthermore, a VSC allows fast, accurate and independent active and reactive power flow control. However, a VSC is a double- input double-output non-linear control objet, therefore; nonlinear control strategies can be useful in order to obtain desired behaviors. In particular, knowledge based fuzzy controller does not require a mathematic model of the system it just relies on a qualitative knowledge about the behavior of the system. Moreover, the same fuzzy controller can be applied to any VSC with any power ratio. This paper presents the analysis, design and results obtained by using fuzzy controllers for regulating the active and reactive power flow in a Voltage Source Converter (VSC) connected to the utility grid. Additionally, it is shown that the transitory and stationary response of the VSC depends on the shape of membership functions at different power ratios. Digital simulation was performed in order to verify the behavior of the controller under different power levels. Finally, a VSC prototype with fuzzy controllers was tested experimentally.

Adaptive Successive Interference Cancelation Using Fuzzy Derived Weights

In wireless communication interference resulting from spatial or temporal sharing of spectrum places a limit on the performance. This paper studies adaptive cancelation of interference by serial (successive) interference cancelation (SIC) technique that uses fuzzy derived cancelation weights. In the past the fuzzy technique has been applied to parallel interference cancelation but not to SIC. The fuzzy inference system uses SNR to estimate the stage interference cancelation weights in multistage SIC rather than selecting them arbitrarily as has been the case in the past. The performance of the fuzzy weighted SIC system proposed herein is evaluated for different number of stages and shapes of membership functions. The simulation results on the system performance are presented in the presence of channel fading and noise. The results show that for overloaded systems, the fuzzy-based system results in a performance that approaches that of the de-correlator detector. This was not possible for a SIC system with arbitrarily chosen weights. The results also conclude that selection of different shapes of membership functions does not make any noticeable difference to the performance.

Inescapable-set Estimation for Nonlinear Systems with Non-vanishing Disturbances

Abstract A common approach for stabilization and performance objectives in control design for TS/polynomial fuzzy models is to solve an LMI problem which proves global stability and an upper bound of a performance function for any shape of membership functions. However, fuzzy models are local and, obviously, a larger modelling region yields lower performance bounds. In systems subject to disturbances, determining the optimal size of the modelling region is needed in order to avoid small ones (useless, as state escapes from them) or overly large ones (suboptimal performance). This paper addresses such problem and suggests a multicriteria fuzzy controller strategy design in order to ensure disturbance rejection performance without leaving a particular modelling region.

Modelling and predicting surface finish in turning using an adaptive neuro-fuzzy inference system

Surface finish is a key factor in the machining process, and it is used to evaluate and determine the quality of a product. Therefore, modelling and predicting of surface finish of a workpiece in turning play an important role in manufacturing industry since turning is most common machining operation. This paper illustrates the application of adaptive neuro-fuzzy inference system ANFIS for modelling and predicting the surface finish in turning operation for set of given cutting parameters, namely spindle speed, feed rate and depth of cut. Three different membership functions, triangular, trapezoidal and generalised bell shaped, were adopted during the hybrid-training process i.e., combination of backpopagation gradient descent method and least square method of ANFIS in order to compare the prediction accuracy of surface finish by the three membership functions. The predicted surface finish values obtained from ANFIS were compared with experimental data. The comparison indicates that the adoption of triangular, trapezoidal and generalised bell shaped membership functions in proposed system achieved satisfactory accuracy. The generalised bell-shaped membership function in ANFIS achieves slightly higher prediction accuracy than other membership functions.

Decoupled Third-order Fuzzy Sliding Model Control For Cart-Inverted Pendulum System

Cart Inverted Pendulum (CIP) system is a benchmark problem in nonlinear automatic control. It is commonly used to verify the robustness of any proposed nonlinear controller. CIP is mostly represented by two second order differential equations to avoid complexity due to the DC motor dynamics. This representation is not practical for the real CIP dynamics and might lead to instability. Therefore in this paper, two third-order differential equations were derived to combine the pendulum system and DC motor dynamics to have a more realistic mathematical model. Friction between the cart and rail was included in the system equations through a nonlinear friction model. To stabilize the obtained nonlinear electromechanical CIP model, a third-order Fuzzy Sliding Mode Controller (FSMC) was designed. The chattering of the control signal was eliminated using general bill shape membership functions for the Fuzzy controller. Simulation results proved the robustness of the proposed FSMC over Linear Quadratic Regulator Controller (LQRC). For instant, the overshoot in the cart position response was reduced by 300%.

Evolutionary Tuning of Fuzzy Rule Base Systems for Nonlinear System Modelling and Control

Fuzzy systems generally works based on expert knowledge base. Fuzzy Expert knowledge base derived from the heuristic knowledge of experts or experience operators in the form of fuzzy control rules and membership functions (MFs). The major difficulties for design ing a fuzzy models and controllers are identify the optimized fuzzy rules and their corresponding shape, type and distribution of MFs. Moreover, the numbers of fuzzy control rules increases exponentially with the number of input output variables related to the control system. For this reason it is very difficult and time consuming for an expert to identify the complete rule set and shape of MFs for a complex control system having large number of input and output variables. In this paper, we propose a methodcalled evolutionary fuzzy system for tuning the parameters of fuzzy rules and adjust the shape of MFs through evolutionary algorithms in order to design a suitable and flexible fuzzy models and controller for complex systems. This paper also presents new flexible encoding method methods for evolutionary algorithms. In evolutionary fuzzy system, the evolutionary algorithms is adapted in two different ways Firstly, generating the optimal fuzzy rule sets including the number of rules inside it and secondly, selecting the optimum shape and distribution of MFs for the fuzzy control rules. In order to evaluate the validity and performance of the proposed approach we have designed a test strategy for the modeling and control of nonlinear systems. The simulation re sults show the effectiveness of our method and give better performance than existing fuzzy expert systems.

Intelligent PI Fuzzy Control of An Electro-Hydraulic Manipulator

The development of a fuzzy-logic controller for a class of industrial hydraulic manipulator is described. The main element of the controller is a PI- type fuzzy control technique which utilizes a simple set of membership functions and rules to meet the basic control requirements of such robots. Using the triangle shaped membership function, the position of the servocylinder was successfully controlled. When the system parameter is altered, the control algorithm is shown to be robust and more faster compared to the traditional PID controller. The robustness and tracking ability of the controller were demonstrated through simulations.

Axiomatic of Fuzzy Complex Numbers

Fuzzy numbers are fuzzy subsets of the set of real numbers satisfying some additional conditions. Fuzzy numbers allow us to model very difficult uncertainties in a very easy way. Arithmetic operations on fuzzy numbers have also been developed, and are based mainly on the crucial Extension Principle. When operating with fuzzy numbers, the results of our calculations strongly depend on the shape of the membership functions of these numbers. Logically, less regular membership functions may lead to very complicated calculi. Moreover, fuzzy numbers with a simpler shape of membership functions often have more intuitive and more natural interpretations. But not only must we apply the concept and the use of fuzzy sets, and its particular case of fuzzy number, but also the new and interesting mathematical construct designed by Fuzzy Complex Numbers, which is much more than a correlate of Complex Numbers in Mathematical Analysis. The selected perspective attempts here that of advancing through axiomatic descriptions.

Autonomous emotion development using incremental modified adaptive neuro-fuzzy inference system

In this paper, we propose an autonomous emotion development system with incremental learning for interacting with human subjects, and autonomously understanding the emotional status of humans. For the understanding of human emotion the proposed system needs human-like visual senses perceiving natural scenes as stimuli. According to the relationship between emotional factors and characteristics of an image, we incorporate the fuzzy concept to extract emotional features using L⁎C⁎H⁎ color and orientation information. Additionally, it can sense inputs that have no analog in human senses-reading brain signals in human subjects. We consider the electroencephalography (EEG) signals which are stimulated by natural stimuli to form the semantic emotional features as well. We develop a novel adaptive neuro-fuzzy inference system (ANFIS) based on an incremental learning algorithm to autonomously develop the capability of understanding complex emotions. The proposed incremental modified ANFIS needs only the newly arrived data to adjust the shape of Gaussian membership functions with full covariance matrix, to generate new membership functions or new rules for labeling emotion according to the characteristics of the new data. Utilizing the developmental process, the proposed system can autonomously develop the mental ability to understand more complex human emotions by mining the characteristics of emotional features and interacting with human subjects.

A General Approach for Solving Assignment Problems Involving with Fuzzy Cost Coefficients

Assignment problem is one of the most-studied, well known and important problems in mathematical programming. In this paper two different type of assignment problems are discussed: conventional and fuzzy assignment problem. In conventional assignment problem, cost is always certain. This paper develops an approach to solve the fuzzy assignment problem where cost is not deterministic numbers but imprecise ones. Here, the elements of the cost matrix of the assignment problem are triangular fuzzy numbers. Its triangular shaped membership function is defined. The optimal solution of fuzzy assignment problem is obtained successfully by using this approach. Compared with the result of conventional assignment problem, the result obtained by our approach is more advantaged for decision-makers. Finally, to show the efficiency of the proposed approach, the problem is demonstrated by one numerical example.