Membership Functions Of Linguistic Terms Research Articles

An Adaptive Hybrid Model for Determining Subjective Causal Relationships in Fuzzy System Dynamics Models for Analyzing Construction Risks

Modeling risk management systems in construction projects is a complex process because of various internal and external factors and their interrelationships. Fuzzy system dynamics (FSD) have been commonly employed to model and analyze construction risk management systems. To run FSD simulation models, all hard (objective) and soft (subjective) causal relationships between variables must be quantified. However, a research gap exists regarding structured methods for constructing soft causal relationships in FSD models. This paper proposes an adaptive hybrid model consisting of fuzzy analytical hierarchy process, weighted principle of justifiable granularity, and fuzzy aggregation operators to determine crisp values of causality degree for soft (subjective) causal relationships in FSD modeling of construction risk analysis. The proposed model is implemented in analyzing construction risks of a windfarm project to illustrate its applicability. The proposed model generates two results: (1) optimized membership functions for linguistic terms representing the causality degree of soft relationships and (2) the crisp value for the causality degree of soft relationships. The contribution of study is to propose a structured model to improve efficiency and effectiveness of developing FSD quantitative modeling by addressing soft causal relationships between different variables in FSD models and considering multiple risk expertise of heterogeneous experts in construction risk assessment.

SEARCH FOR OPTIMAL FUNCTIONS OF FUZZY SYSTEMS BASED ON BIOINSPIRED EVOLUTIONARY ALGORITHMS

Contemporary research in the field of creation and development of intelligent systems based on fuzzy logic is carried out mainly in the direction of developing highly efficient methods for their synthesis and structural-parametric optimization. In recent years, due to the intensive development of information technologies and computer hardware, bioinspired intelligent techniques of global search are quite promising for solving problems of synthesis and optimization of fuzzy systems, which include evolutionary and swarm methods, that simulate the processes of natural selection, as well as collective behavior of various groups of social animals, insects and microorganisms in nature. This paper is devoted to the development and study of a method of optimal membership functions search for fuzzy systems based on bioinspired evolutionary algorithms of global optimization. The obtained method allows finding the optimal membership functions of linguistic terms at solving the compromise problems of multicriteria structural optimization of various fuzzy systems in order to increase their efficiency, as well as to reduce the degree of complexity of further parametric optimization. In the proposed method for finding the global optimum of the problem being solved, the iterative procedures are carried out on the basis of combination of several different bioinspired evolutionary algorithms with subsequent analysis of the results obtained and the choice of the best variant of the membership function vector. The paper outlines the theoretical foundations and information model for the implementation of the computational step-by-step method for structural optimization of fuzzy systems, as well as presents various options for carrying out its search procedures. In particular, the features of the application and adaptation to the search problem to be solved of such bioinspired evolutionary algorithms as genetic, artificial immune systems and biogeographic are discussed.

Поиск оптимальных функций принадлежности нечетких систем на основе биоинспирированных эволюционных алгоритмов. Часть 2. Реализация метода и исследование его эффективности

Исследована эффективность метода поиска оптимальных функций принадлежности нечетких систем на основе биоинспирированных эволюционных алгоритмов глобальной оптимизации. Предложенный метод позволяет находить оптимальные функции принадлежности лингвистических термов при решении компромиссной задачи минимизации целевой функции и уменьшения вычислительных затрат в процессе дальнейшей параметрической оптимизации нечетких систем. Для исследования эффективности рассматриваемого метода проведен поиск оптимальных функций принадлежности для нечеткого регулятора системы управления многоцелевым мобильным роботом, предназначенный для перемещения по наклонным и вертикальным ферромагнитным поверхностям, с реализацией данного метода на основе трех биоинспирированных эволюционных алгоритмов: , искусственных иммунных систем, биогеографический Анализ полученных результатов компьютерного моделирования показал, что применение предложенного метода поиска оптимальных функций принадлежности позволяет существенно повысить эффективность управления мобильным роботом, а также уменьшить общее количество параметров при последующей параметрической оптимизации лингвистических термов, что подтверждает высокую эффективность разработанного метода. .

An Interval Type-2 Fuzzy Risk Analysis Model (IT2FRAM) for Determining Construction Project Contingency Reserve

Determining contingency reserve is critical to project risk management. Classic methods of determining contingency reserve significantly rely on historical data and fail to effectively incorporate certain types of uncertainties such as vagueness, ambiguity, and subjectivity. In this paper, an interval type-2 fuzzy risk analysis model (IT2FRAM) is introduced in order to determine the contingency reserve. In IT2FRAM, the membership functions for the linguistic terms used to describe the probability, impact of risk and the opportunity events are developed, optimized, and aggregated using interval type-2 fuzzy sets and the principle of justifiable granularity. IT2FRAM is an extension of a fuzzy arithmetic-based risk analysis method which considers such uncertainties and addresses the limitations of probabilistic and deterministic techniques of contingency determination methods. The contribution of IT2FRAM is that it considers the opinions of several subject matter experts to develop the membership functions of linguistic terms. Moreover, the effect of outlier opinions in developing the membership functions of linguistic terms are reduced. IT2FRAM also enables the aggregation of non-linear membership functions into trapezoidal membership functions. A hypothetical case study is presented in order to illustrate the application of IT2FRAM in Fuzzy Risk Analyzer© (FRA©), a risk analysis software.

Fuzzy environmental monitoring for the quality assessment: Detailed feasibility study for the Karun River basin, Iran

Nowadays, fuzzy logic has been successfully employed to handle the environmental issues. Due to inflexible classifications and interpretative difficulties of crisp data involved in the classic water quality index (WQI), employment of fuzzy logic in water resources problems has been repeatedly recommended for decision support in ecological assessment. As a mixed-ability method, it can both provide balance in suitable and unsuitable observations, which results in incompatible outcomes and can interpret social, economic, and biological effects as membership functions of linguistic terms. Accordingly, this study aims at evaluating the Karun River water quality, based on Mamdani fuzzy approach using developing a fuzzy water quality index (FWQI), a hierarchical approach was conducted to avoid curse of dimensionality in fuzzy rules and to take the relationships between parameters in to account as well 21 parameters in the form of physicochemical, chemical, biological and heavy metal fuzzy groups were processed based on trapezoidal membership functions and the rulesets for considering the relationships between parameters. A global FWQI of 36.78 ± 2.17 has been estimated based on 2010 to 2015 data, while the assessment with NSFWQI and CCMEWQI indices scored 41.8 ± 1.33 and 33.65 ± 2.87, respectively. Based on ANOVA test, changes in global FWQI were not significant in sampling stations. Moreover, results show degradation of the water quality from upstream to downstream of the Karun watershed. These results put forward an argument in necessity of regeneration of the Karun water quality to improve ecological conditions in the region.

Identifying Risk Factors of Diabetes using Fuzzy Inference System

Identification of the real risk factors of diabetes is still very much inconclusive. In this paper, fuzzy rules based system was devised to identify risk factors of diabetes. The system consists of five input variables: Body Mass Index, age, blood pressure, Creatinine, and serum cholesterol and one output variable: level of risk. Three Gaussian membership functions for linguistic terms are defined for each input variable. The level of risk is defined using three triangular membership functions to represent output variable. Based on the information from patients’ clinical audit reports, the system was used to classify the level of risk of fifty patients that currently undergoing regular diagnosis for diabetes treatment. The system successfully classified the risk into three levels of Low, Medium and High where three main contributing factors toward developing diabetes were identified.<br /> The three risk factors are age, blood pressure and serum cholesterol.<br /> The multi-input system that characterised by IF-THEN fuzzy rules provide easily interpretable result for identifying predictors of diabetes. Research to establish reproducibility and validity of the findings are left for future works.

DEVELOPMENT OF FUZZY NEURAL NETWORK FOR THE INTERPRETATION OF THE RESULTS OF DISSOLVED IN OIL GASES ANALYSIS

Purpose. The purpose of this paper is a diagnosis of power transformers on the basis of the results of the analysis of gases dissolved in oil. Methodology. To solve this problem a fuzzy neural network has been developed, tested and trained. Results. The analysis of neural network to recognize the possibility of developing defects at an early stage of their development, or growth of gas concentrations in the healthy transformers, made after the emergency actions on the part of electric networks is made. It has been established greatest difficulty in making a diagnosis on the criterion of the boundary gas concentrations, are the results of DGA obtained for the healthy transformers in which the concentration of gases dissolved in oil exceed their limit values, as well as defective transformers at an early stage development defects. The analysis showed that the accuracy of recognition of fuzzy neural networks has its limitations, which are determined by the peculiarities of the DGA method, used diagnostic features and the selected decision rule. Originality. Unlike similar studies in the training of the neural network, the membership functions of linguistic terms were chosen taking into account the functions gas concentrations density distribution transformers with various diagnoses, allowing to consider a particular gas content of oils that are typical of a leaky transformer, and the operating conditions of the equipment. Practical value. Developed fuzzy neural network allows to perform diagnostics of power transformers on the basis of the result of the analysis of gases dissolved in oil, with a high level of reliability.

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.

Extracting complex linguistic data summaries from personnel database via simple linguistic aggregations

A linguistic data summary of a given data set is desirable and human consistent for any personnel department. To extract complex linguistic data summaries, the LOWA operator is used from fuzzy logic and some numerical examples are also provided in this paper. To obtain a complex linguistic data summary with a higher truth degree, genetic algorithms are applied to optimize the number and membership functions of linguistic terms and to select a part of truth degrees for aggregations, in which linguistic terms are represented by the 2-tuple linguistic representation model.