Use Of Fuzzy Logic Systems Research Articles

An intelligent fuzzy robustness ZNN model with fixed‐time convergence for time‐variant Stein matrix equation

On account of the rapid progress of zeroing neural network (ZNN) and the extensive use of fuzzy logic system (FLS), this article proposes an intelligent fuzzy robustness ZNN (IFR-ZNN) model and applies it to solving the time-variant Stein matrix equation (TVSME) problem. Be different from ZNN models before, the IFR-ZNN model uses a fuzzy parameter as the design parameter and adopts a first proposed improved nonlinear piecewise activation function. Particularly, the FLS that generates the fuzzy parameter utilizes an improved membership function of nonuniform distribution which can improve the adaptability and robustness of the IFR-ZNN model. Based on the above two optimizations, the proposed IFR-ZNN model possesses three significant advantages: (1) fixed-time convergence independent of initial states; (2) superior robustness to tolerate two kinds of noises simultaneously; and (3) better adaptiveness based on computational error. Besides, the upper bounds of fixed-time convergence of the IFR-ZNN model under noisy or non-noisy situations are calculated theoretically, and the stability as well as the excellent adaptability are analyzed in detail. Finally, simulation comparison results manifest the availability and meliority of the proposed IFR-ZNN model in solving the TVSME problem.

Design of fuzzy logic system framework using evolutionary techniques

Designing fuzzy logic system is one of the most popular and research-demanding NP-hard problems. It involves numerous parameters like shape and location of fuzzy sets, antecedents and consequents of fuzzy rule base and other strategic parameters like aggregation, implication and defuzzification methods. Time series forecasting has also become increasingly popular for the applications like share market prediction, weather forecasting. Many researchers have investigated the use of fuzzy logic system for forecasting of time series. In this paper, the authors have investigated the design framework of fuzzy logic systems for forecasting benchmark Mackey–Glass time series. Designing fuzzy logic systems is a class of NP-hard problems which is evolved using most popular and recent evolutionary algorithms. Authors have evolved fuzzy logic system using genetic algorithm, particle swarm optimization, artificial bee colony optimization, firefly algorithm and whale optimization algorithm. Finally, from simulations, it is found that whale optimization algorithm requires less time and shows fuzzy system predictions are more precise than others.

Multimedia detection algorithm of malicious nodes in intelligent grid based on fuzzy logic

Intelligent grid transmits data by means of wireless communication. The wireless communication network is vulnerable to multiple kinds of network attacks. Trust model is considered to be an important way to defend against malicious network attacks in large-scale communication networks. Concerning the problem of node attack in smart grid, a trust model FLTM based on fuzzy logic is proposed. FLTM model makes full use of fuzzy logic system to deal with uncertainties, estimates the overall trust value of nodes, and then detects malicious nodes. Taking direct trust, indirect trust and historical trust as inputs, the output is the overall trust of the node. Experimental data show that the proposed FLTM model can detect malicious nodes effectively. In the later stage, the FLTM model is applied to routing, thus improving the performance of data transmission.

Towards the use of fuzzy logic systems in rotary wing unmanned aerial vehicle: a review

In recent times, technological advancement boosts the desire of utilizing the autonomous Unmanned Aerial Vehicle (UAV) in both civil and military sectors. Among various UAVs, the ability of rotary wing UAVs (RUAVs) in vertical take-off and landing, to hover and perform quick maneuvering attract researchers to develop models fully autonomous control framework. The majority of first principle techniques in modeling and controlling RUAV face challenges in incorporating and handling various uncertainties. Recently various fuzzy and neuro-fuzzy based intelligent systems are utilized to enhance the RUAV’s modeling and control performance. However, the majority of these fuzzy systems are based on batch learning methods, have static structure, and cannot adapt to rapidly changing environments. The implication of Evolving Intelligent System based model-free data-driven techniques can be a smart option since they can adapt their structure and parameters to cope with sudden changes in the behavior of RUAVs real-time flight. They work in a single pass learning fashion which is suitable for online real-time deployment. In this paper, state of the art of various fuzzy systems from the basic fuzzy system to evolving fuzzy system, their application in a RUAV namely quadcopter with existing limitations, and possible opportunities are analyzed. Besides, a variety of first principle techniques to control the quadcopter, their impediments, and conceivable solution with recently employed evolving fuzzy controllers are reviewed.

An Adaptive Neuro-Fuzzy Inference System in Assessment of Technical Losses in Distribution Networks

The losses in distribution networks have always been key elements in predicting investment, planning work, evaluating the efficiency and effectiveness of a network. This paper elaborates on the use of fuzzy logic systems in analyzing the data from a particular substation area predicting losses in the low voltage network. The data collected from the field were obtained from the Automatic Meter Reading (AMR) and Automatic Meter Management (AMM) systems. The AMR system is fully implemented in EPHZHB and integrated within the network infrastructure at secondary level substations 35/10kV and 10(20)/0.4 kV. The AMM system is partially implemented in the areas of electrical energy consumers; precisely, in accounting meters. Daily information gathered from these systems is of great value for the calculation of technical and non-technical losses. Fuzzy logic in combination with the Artificial Neural Networks implemented via the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used. Finally, FIS Sugeno, FIS Mamdani and ANFIS are compared with the measured data from smart meters and presented with their errors and graphs.

An Adaptive Neuro-Fuzzy Inference System in Assessment of Technical Losses in Distribution Networks

The losses in distribution networks have always been key elements in predicting investment, planning work, evaluating the efficiency and effectiveness of a network. This paper elaborates on the use of fuzzy logic systems in analyzing the data from a particular substation area predicting losses in the low voltage network. The data collected from the field were obtained from the Automatic Meter Reading (AMR) and Automatic Meter Management (AMM) systems. The AMR system is fully implemented in EPHZHB and integrated within the network infrastructure at secondary level substations 35/10kV and 10(20)/0.4 kV. The AMM system is partially implemented in the areas of electrical energy consumers; precisely, in accounting meters. Daily information gathered from these systems is of great value for the calculation of technical and non-technical losses. Fuzzy logic in combination with the Artificial Neural Networks implemented via the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used. Finally, FIS Sugeno, FIS Mamdani and ANFIS are compared with the measured data from smart meters and presented with their errors and graphs.

Use of Fuzzy Logic Systems for Assessment of Primary Faults

Abstract In electric power systems, grid elements are often subjected to very complex and demanding disturbances or dangerous operating conditions. Determining initial fault or cause of those states is a difficult task. When fault occurs, often it is an imperative to disconnect affected grid element from the grid. This paper contains an overview of possibilities for using fuzzy logic in an assessment of primary faults in the transmission grid. The tool for this task is SCADA system, which is based on information of currents, voltages, events of protection devices and status of circuit breakers in the grid. The function model described with the membership function and fuzzy logic systems will be presented in the paper. For input data, diagnostics system uses information of protection devices tripping, states of circuit breakers and measurements of currents and voltages before and after faults.

Using fuzzy logic to model the behavior of residential electrical utility customers

Peaks and valleys affecting the efficiency of the power system can be detected by analyzing the load curve. These oscillations are caused by changes in consumer behavior, mainly consumers in the residential sector. This paper presents the use of fuzzy logic systems to model human behavior related to activation of appliances and lighting at home. Based on this model, the hourly activation profile for each appliance can be obtained and, subsequently, the load curve of the residential sector can be calculated. This model aims at contributing to the simulation of strategies for demand-side management.

The Use of Fuzzy Logic System in Determining Preferences toward GUI Design for E-Commerce Website

This paper presents the findings of a survey that correlate human personality types with the preferences towards graphical user interface (GUI) design for e–commerce website. Myers–Briggs Type Indicator (MBTI) inventory is the personality instrument used to conduct personality test on human beings in this study. The targeted sample is the students from UCSI University KL Campus. They are first group into one of the sixteen MBTI personality types, and then they are further grouped into one of the four Myers–Briggs temperaments. 2 types of surveys are imposed: the Personality and Preferences Questionnaire (PPQ) which aim to figure out the correlation; and User Evaluation Questionnaire (UEQ) which is conducted online is used to collect user feedbacks on the resultant website prototypes. Fuzzy logic theory together with the data collected during PPQ is implemented into ANFIS systems to come out with inferences about whether or not the suggested correlation exists. Conclusion drawn from this study is that human's personality type does affect some of the preference towards website elements (website layout and font type used), but not all.

Fuzzy logic for piping risk assessment (pfLOPA)

This paper explores the application of the fuzzy logic for risk assessment of major hazards connected with transportation of flammable substances in long pipelines. As a basis for risk assessment, the framework of the fuzzy Layer of Protection Analysis (fLOPA) was used. fLOPA presents a new approach to risk assessment based on two assumptions: 1. different effects of the layer of protection functions on particular elements of the risks (frequency and severity of consequence), and 2. the application of fuzzy logic system (FLS) composed of three elements: fuzzification, inference process and defuzzification. A further calculation follows LOPA methodology with the use of fuzzy logic system where fuzzy risk matrix is used for risk assessment. A typical case study comprising section of a long pipeline failure is performed and a comparison between the classical LOPA approach and fuzzy approach is made.

Fuzzy logic approach to predict stress–strain curves of steel fiber-reinforced concretes in compression

In this study, a new approach is developed for predicting stress–strain curve of steel fiber-reinforced concrete (SFRC) under compression, by use of fuzzy logic system (FLS). In the coverage of study, firstly, experimental studies were carried out. Cylindrical specimens were prepared in size of Ø150×300 mm with and without steel fiber. Two different steel fiber types (both are hooked end) were used as ratios 0 (control), 15, 30, 45 and 60 kg/m 3. The stress–strain curves were defined for 28 ages of the cylindrical specimens. Secondly, the stress–strain curves for SFRC were modeled by use of fuzzy logic approach, and the results that were obtained from experiments and modeling were compared. As a result close relationship between both results was seen.