Fuzzy Input Variables Research Articles

Recognition and Classification of Power Quality Disturbances on the basis of Pattern Linguistic Values

This paper presents a new recognition and classification method for power quality (PQ) disturbances on the basis of pattern linguistic values. This method solves the difficulty of recognizing disturbances rapidly and accurately by using fuzzy logic. This method uses classification disturbance patterns to define the linguistic values of fuzzy input variables and used the input variables of corresponding disturbance pattern to set membership functions. This method also sets the fuzzy rules by analyzing the distribution regularities of the input variable values. One characteristic of this method is that the linguistic values of fuzzy input variables and the setting of membership functions are not only related to the input variables but also to the character of classification disturbance and the classification results. Furthermore, the number of fuzzy rules is equal to the number of disturbance patterns. By using this method for disturbance classification, the membership function and design of fuzzy rules are directly related to the objective of classification, thus effectively reducing the complexity of the design process and yielding accurate classification results. The classification results of the simulation and measured data verify the feasibility and effectiveness of this method.

Simulation-based construction productivity forecast using Neural-Network-Driven Fuzzy Reasoning

Abstract Fuzzy models and Artificial Neural Network (ANN) systems are two well-known areas of soft-computing that have significantly helped researchers with decision-making under uncertainties. Uncertainty, an ever-present factor in construction projects, has made such intelligent systems very attractive to the construction industry. Estimating the productivity of construction operations, as a basic element of project planning and control, has become a remarkable target for forecasting models. A glimpse into this interdisciplinary field of research exposes the need for a system, that (1) models the effect of qualitative and quantitative variables on construction productivity with an improved accuracy of estimation and (2) has the ability to deal with both crisp and fuzzy input variables in one single framework. Neural-Network-Driven Fuzzy Reasoning (NNDFR), as one of the hybrid intelligent structures, displays a great potential for modeling datasets among which clear clusters are recognizable. The weakness of NNDFR in auto-tuning the design of fuzzy membership functions along with this model's insufficient attention to the optimization of number of clusters has created an area for further research. In this paper, the parameters (fuzzifier and number of clusters) of the proposed system are optimized by using Genetic Algorithm (GA) to fine-tune the system for the highest possible level of accuracy that can be exploited for productivity estimation. The proposed model is also capable of dealing with a combination of crisp and fuzzy input variables by using a hybrid modeling approach based on the application of the alpha-cut technique. The developed model helps researchers and practitioners use historical data to forecast the productivity of construction operations with a level of accuracy greater than what could be offered by traditional techniques.

Robust Regression Analysis with LR-Type Fuzzy Input Variables and Fuzzy Output Variable

In this paper, we propose a fuzzy linear regression model with LR-type fuzzy input variables and fuzzy output variable, the fuzzy extent of which may be different. Then we give the iterative solution of the proposed model based on the Weighted Least Squares estimation procedure. Some properties of the estimates are proved. We also define suitable goodness of fit index and its adjusted version useful to evaluate the performances of the proposed model. Based on the Least Median Squares-Weighted Least Squares (LMS-WLS) estimation procedure, we give robust estimation steps for the proposed model. Compared with the well-known fuzzy Least Squares method, the effectiveness of our model on reducing the outliers influence is shown by using two examples.

A Novel Fuzzy Logic Model to Identify Closeness for Alias Detection

The detection of person alias names are important for improving the accuracy of the information quality. The fuzzy-based decision system is proposed for alias detection, which is a rule-based system that uses fuzzy logic to make a decision about the closeness between the given name pairs. A fuzzy logic is formulated by a set of linguistic variables based on feature’s score value. An entity pair’s association score values are calculated using string and link-based features like Hamming Distance, Leventein Distance, Normalized String Edit Distance, Common Friends, Normalized Dot Product and Co-occurrence Relevance and an output variable accuracy as closeness. These features are transformed into fuzzy input variables and designed with proper membership functions. The proposed novel fuzzy inference system gives the decision of aliases closeness in the form of crisp values ranging from 0 to 1. In this work, the model achieves upto 90% accuracy compared to estimated accuracy.

A Study on the Fuzzy-Logic-Based Solar Power MPPT Algorithms Using Different Fuzzy Input Variables

Maximum power point tracking (MPPT) is one of the key functions of the solar power management system in solar energy deployment. This paper investigates the design of fuzzy-logic-based solar power MPPT algorithms using different fuzzy input variables. Six fuzzy MPPT algorithms, based on different input variables, were considered in this study, namely (i) slope (of solar power-versus-solar voltage) and changes of the slope; (ii) slope and variation of the power; (iii) variation of power and variation of voltage; (iv) variation of power and variation of current; (v) sum of conductance and increment of the conductance; and (vi) sum of angles of arctangent of the conductance and arctangent of increment of the conductance. Algorithms (i)–(iv) have two input variables each while algorithms (v) and (vi) use a single input variable. The fuzzy logic MPPT function is deployed using a buck-boost power converter. This paper presents the details of the determinations, considerations of the fuzzy rules, as well as advantages and disadvantages of each MPPT algorithm based upon photovoltaic (PV) cell properties. The range of the input variable of Algorithm (vi) is finite and the maximum power point condition is well defined in steady condition and, therefore, it can be used for multipurpose controller design. Computer simulations are conducted to verify the design.

Health Effects of Electromagnetic Pollution Modeling Using Fuzzy Inference System

Electromagnetic radiations has some negative effects on public health. The main sources of these radiations are mobile phones, base stations, Wi-Fi,medical equipment and domestic appliances. Hundreds of studies on the health effects of electromagnetic fields have very mixed results. This requires us to use an analytical technique based on the principles of artificial intelligence, including the principles of fuzzy logic. This work aims to predict the health effects on exposed subjects. Because these reactions are characterized by uncertainty and imprecision, we found it useful to analyze these data by the techniques of fuzzy logic. The structure of the block diagram of our analysis system consists of seven inputs (Age, Sex,Distance, Frequency, Induction, Duration and Type of exposure) and one output (health effects).These are inspired from epidemiological studies that aim to find the link between exposures to electromagnetic field on the one hand and biological or pathological effects on the other. After fuzzification of inputs and output, several rules of inference, connecting the fuzzy input variables to the fuzzy output variable were developed in the form: IF “predicate” THEN “conclusion”. The next step is the combination of all the rules and defuzzification of parameters. Finally, we can assume the effects of electromagnetic fields in other cases not investigated. Fuzzy logic deals with uncertainty is perfectly appropriate in our case in which a fuzzy algorithm is proposed to predict the health effects on exposed subjects from the input variables.

English

In this paper proposes a simple but effective method of electric brake with energy regeneration for a brushless dc motor, of electric vehicle (EV). BLDC motor control utilizes the traditional proportional-integralderivative (PID) control and the distribution of braking force adopts fuzzy logic Conventionally, EVs use mechanical brake to increase the friction of wheel for the deceleration purpose. However, from the viewpoint of saving energy, the mechanical brake dissipates much energy since the EV’s kinetic energy is converted into the thermal one. In view of this, this paper discusses how to convert the kinetic energy into the electrical one that can be recharged to the battery. Thus, both the electric brake and energy regeneration are achieved. In comparison to other solutions, the new solution has better performance in regard to realization, robustness and efficiency. In this paper, we have chosen the three most important factors: SOC, speed and brake strength as the fuzzy control input variables. We have found that RBS can obtain appropriate brake current, which is used to produce brake torque. At the same time, we have adopted PID control to adjust the BLDC motor PWM duty to obtain the constant brake torque. PID control is faster than fuzzy control so the two methods combined together can realize the smooth transition since the braking kinetic energy is converted into the electrical energy and then returns to the battery, the energy regeneration could increase the driving range of an EV.

A NOVEL MULTISTAGE FUZZY CONTROLLER FOR FACTS STABILIZATION SCHEME FOR SMIB AC SYSTEM

The paper presents a novel Hybrid-FACTS Based Stabilization Scheme controlled by a hierarchicaltwo-stage fuzzy logic (HFLC)-multi loop dynamic error driven controller. The proposed schemeincludes separate Fuzzy control stages for the PD and PID parts to ensure robust and effective dynamicspeed control and efficient energy utilization. The PD fuzzy stage used the global error and change oferror as the fuzzy input variables. The second stage is the PID-FLC regulation which utilizes theoutput of the PD-FLC stage and the integral of the global error as input fuzzy variables. Thesimulation results validate the proposed control scheme effectiveness and robustness with efficientenergy utilization, improved power quality and power factor at the Common AC Bus and load bus. ADigital simulation model of the proposed system is developed in Matlab/Simulink/Simpower SoftwareEnvironment using operational dynamic blocks available in Simulink library.

Cluster Head Selection Protocol using Fuzzy Logic for Wireless Sensor Networks

trends in field of wireless networks is setting up Wireless Sensor Networks that, senses specified parameter(s) related to environment; processes sensed data and wirelessly communicates it to a base station. Such networks open up a whole new range of applications, including precision agriculture, monitoring and tracking vehicles, animals and humans, battle-field surveillance, civil structural monitoring etc. All these applications require extended network lifetime, scalability, and traffic balancing among nodes in the network. Clustering is one of the effectual techniques for achieving these requirements. In clustering, geographically adjacent nodes are organized into virtual groups called clusters. One of the cluster node acts as a cluster head and rest as cluster members. This paper presents Cluster Head selection protocol using Fuzzy Logic (CHUFL). It uses node's parameters like: residual energy, reachability from its neighborhood, quality of communication link with its neighborhood and distance from base station as fuzzy input variables for cluster head selection. A comparative analysis of CHUFL with cluster head selection mechanism using fuzzy logic by Indranil et. al.; Cluster Head Election mechanism using Fuzzy logic (CHEF) by Kim et. al. and cluster head selection method for wireless sensor networks based on fuzzy logic by J. Anno et. al. shows that CHUFL is up to 20 % more energy efficient and sends 72% more packets to base station compared to protocol by J. Anno et. al., one of the energy efficient clustering protocol.

Fuzzy Logic Approach to Credit Scoring for Micro Finance in Ghana: A Case Study of KWIQPLUS Money Lending

This paper presents a fuzzy logic approach to credit scoring for Micro Finance.The research was necessitated as a result of the inability of many Micro Finance Institutions in Ghana to recover loans from their clients which is leading to their eventual collapse. It has been presumed that proper evaluations are not done by the Micro Finance Institutions thereby advancing loans to wrongful applicants. The main objective of this research was therefore to provide a Fuzzy approach to credit scoring in order to reduce the loan default among the Micro-Finance Institutions so as to ensure their continuous existence. The research used three Fuzzy Input variables with their triangular membership function, an Output variable and twenty-seven fuzzy rules in the development of an evaluation model.

Practical computing with interactive fuzzy variables

The importance of epistemic uncertainty in engineering is being recognized more and more. In this work, epistemic uncertainty is captured through the use of fuzzy variables, i.e. variables that are described in terms of possibility distributions. Taking into account epistemic uncertainty implies that one should also be able to propagate such uncertainty through increasingly complex models. In earlier work, we have developed a general tool, called Fuzzy Calculator, to propagate epistemic uncertainty regarding non-interactive fuzzy input variables. In reality, however, the fuzzy input variables of a model are often interactive. Taking into account this interactivity reduces the uncertainty on the fuzzy output variable and should therefore always be aimed for. However, the full determination of the joint possibility distribution of the fuzzy input variables is not an easy task. In literature, it is often claimed that triangular norms can be used to model interactivity between fuzzy variables. Making use of a generalization of Nguyen's alpha-cut approach, we have developed a general-purpose Fuzzy Calculator that is able to take into account interactivity modelled by a triangular norm. However, the absence of a general strategy to select an appropriate triangular norm often results in the use of other strategies to model interactivity in practice. Therefore, we have also applied our Fuzzy Calculator to a case study where the interactivity between the fuzzy input variables is identified through possibilistic clustering. The results illustrate that this Fuzzy Calculator is an efficient tool to propagate epistemic uncertainty regarding non-interactive as well as interactive fuzzy input variables.

Importance analysis on the failure probability of the fuzzy and random system and its state dependent parameter solution

For structure systems with fuzzy input uncertainty as well as random one, the effects of the two kinds of uncertainties on the failure probability of the structure are studied, and an importance analysis model is established to quantitatively evaluate these effects. Based on the fact that the fuzziness of the output is determined by that of the input, the importance measure is defined to evaluate the effect of the fuzzy input variable. For the random input variable, the established model analyzes its importance from two aspects: (1) its effect on the most plausible value of the failure probability, (2) its effect on the imprecision of the failure probability. In the process of computing the importance measures, the conditional failure probability and unconditional one need to be evaluated, which is time demanding for practical engineering problems. For efficiently performing importance analysis in the presence of the fuzzy input variables and the random ones, the importance sampling (IS) method combined with the state dependent parameter (SDP) method is presented. Several examples show that the established importance analysis model can reflect the effects of the two kinds of input variables on the safety of the structure comprehensively and reasonably, and the presented solution can improve computational efficiency considerably with acceptable precision.

A hybrid particle swarm optimization algorithm for satisficing data envelopment analysis under fuzzy chance constraints

This paper presents a new satisficing data envelopment analysis (DEA) model with credibility criterion, in which the inputs and outputs are assumed to be characterized by fuzzy variables with known membership functions. When the inputs and outputs are mutually independent trapezoidal fuzzy variables, we turn the proposed satisficing DEA model into its deterministic equivalent programming problem. For general fuzzy input and output variables, we design a hybrid particle swarm optimization (PSO) algorithm by integrating approximation method, neural network (NN) and PSO algorithm to solve the proposed DEA model, in which the approximation method is used to compute the credibility functions, NN is used to approximate the credibility functions, and PSO is used to find the optimal solution of the proposed DEA problem. Furthermore, the sensitivity analysis of the proposed model is discussed. Finally, we perform a number of numerical experiments to demonstrate the effectiveness of the hybrid PSO algorithm. The computational results show that the designed hybrid PSO algorithm outperforms the hybrid genetic algorithm (GA) in terms of runtime and solution quality.

Fuzzy Control System of Water Level Based on Fuzzy Reasoning

This paper introduces a control system of water level based on fuzzy control, describes the whole process of fuzzy reasoning. In this control system, the fuzzy controller's input and output variables are determined, and the fuzzy variable description. Then the choice of rules and fuzzy description, establishing fuzzy relationship, and fuzzy inference, and defuzzification.

A Fuzzy Variable Least Core Game for Inter-basin Water Resources Allocation Under Uncertainty

In this paper, a new solution concept, called Fuzzy Variable Least Core (FVLC), is developed for fuzzy cooperative games. The FVLC is able to incorporate fuzzy input variables and result in fuzzy benefit shares of players participating in a coalition. This solution concept is used for water and benefit allocation to water users in inter-basin water transfer systems considering the uncertainties associated with their benefit coefficients. In the proposed water allocation methodology, an Integrated Stochastic Dynamic Programming (ISDP) model is developed to obtain the water rights of players and economic water allocation policies. In the next step, the total net fuzzy benefit of the system is reallocated to water users in an equitable and rational way using a FVLC-based model. In this model, a new algorithm is proposed for converting a multilateral cooperative game with fuzzy variables to some fuzzy bilateral cooperative games, which are solved using the FLVC solution concept. The applicability and efficiency of the proposed methodology is examined by applying it to a large scale inter-basin water transfer project in Iran.

Blind 3D Model Watermarking based on Multi-Resolution Representation and Fuzzy Logic

Insertion of a text message, audio data or/and an image into another image or 3D model is called as a watermarking process. Watermarking has variety of applications like: Copyright Protection, Owner Identification, Copy Protection and Data Hiding etc., depending upon the type of watermark insertion algorithm. Watermark remains in the content after applying various attacks without any distortions. The blind watermarking method used in the system is based on a wavelet transform, a fuzzy inference system and a multi-resolution representation (MRR) of the 3d model. The watermark scrambled by Arnold Transform is embedded in the wavelet coefficients at third resolution level of the MRR. Fuzzy logic approach used in the method makes it to approximate the best possible gain with an accurate scaling factor so that the watermark remains invisible. The fuzzy input variables are computed for each wavelet coefficient in the 3D model. The output of the fuzzy system is a single value which is a perceptual value for each corresponding wavelet coefficient. Thus, the fuzzy perceptual mask combines all these nonlinear variables to build a simple, easy to use HVS model. Results shows that the system is robust against affine transformations, smoothing, cropping and noise attacks.

Application Studies of Fuzzy Control on Adding Medicament in Circulating Water Treatment

Considering the control problem of circulating water system in the power plants with pure delay, inertia, and sensitivity to the environmental temperature, this paper applies the fuzzy algorithm to adding medicament control in circulating water system. The main procedure of fuzzy control algorithm consists of fuzzification, fuzzy inference and defuzzification. The fuzzy input and output variables are chosen as seven classes. Then, according to the actual operation situations and the expert experiences, the fuzzy rules are determined in the form of table. The effectiveness of the proposed fuzzy control method is proved by some practical examples.

Design of Gain Fuzzy Controller for Underwater Trash Detection System Based on FPGA

In order to improve the performance of the underwater trash detection system effectively, the gain fuzzy controller is employed to make programmable gain amplifier work in the optimum status, and its gain will be adjusted according to target distance and echo amplitude in time. The target distance and the echo amplitude are input fuzzy variables, and the amplifier gain is output fuzzy variable. After designing membership functions and fuzzy control rules, defuzzification of the output variable is achieved according to the largest membership degree algorithm. FPGA is used to realize gain fuzzy controller with the inference lookup table, and the system experiments are completed. The results show: the functions of the gain fuzzy controller based on FPGA are implemented correctly, and the design is effectively simplified, and the real-time performance is improved by using the fuzzy inference lookup table.

Propagation of uncertainties in water distribution systems modeling

Optimal design and systematic uncertainty modeling of a municipal Water Distribution System (WDS) aim to minimize operational & construction costs, while meeting demands with required pressure levels and water quality. Study about the propagation of uncertainty through the hydraulic model provides a basis for general model improvement in an efficient and economic way. The present study introduces a novel technique for uncertainty analysis of a WDS, which synchronizes the hydraulic network and water quality solving capabilities of EPANET with an interface to MATLAB. It treats the pipe friction factors and the nodal demands as input fuzzy variables whereas the output fuzzy parameters include pipe discharges, nodal heads and chlorine concentrations. The methodology involves decomposition of the given uncertainties into a set of all possible combinations of input parameter values, numerical analysis of the hydraulic network, calculation of extremities of each unknown variable at each alpha cut level and the f...

Design and Implementation of a Fuzzy Controller for Small Rotation Angles

This paper present an adaptation mechanism for fuzzy logic controller FLC in order to perfect the response performance against small rotation angles of real D.C. motor with unknown parameters. A supervisor fuzzy controller SFC is designed to continuously adjust, on-line, the universe of discourse UOD of the basic fuzzy controller BFC input variables based on position error and change of position error. Performance of the proposed adaptive fuzzy controller is compared with corresponding conventional FLC in terms of several performance measures such rise time, settling time, peak overshoot, and steady state error. The system design and implementation are carried out using LabVIEW 2009 with NI PCI-6251 data acquisition DAQ card. The practical results demonstrate using self tuning FLC scheme grant a better performance as compared with conventional FLC which is incapable of rotating a motor if the rotation angle is being small.