A Multicriteria Fuzzy System Using Residuated Implication Operators and Fuzzy Arithmetic

2007 ◽  
pp. 57-67 ◽  
Author(s):  
Sandra Sandri ◽  
Christophe Sibertin-Blanc ◽  
Vicenç Torra
Keyword(s):  
Fuzzy System ◽  
Fuzzy Arithmetic

scholarly journals A Bi-level Neuro-Fuzzy System Soft Computing for Reservoir Operation

2021 ◽  
Vol 13 (3) ◽  
pp. 224-247
Author(s):  
Mekonnen Redi ◽  
Mihret Dananto ◽  
Natesan Thillaigovindan
Keyword(s):  
Soft Computing ◽  
Water Conservation ◽  
Fuzzy System ◽  
Reservoir Operation ◽  
Ethiopian Rift ◽  
Fuzzy Arithmetic ◽  
Model Parameters ◽  
Main Ethiopian Rift ◽  
Term Operation ◽  
Neuro Fuzzy

Reservoir operation studies purely based on the storage level, inflow, and release decisions during dry periods only fail to serve the optimal reservoir operation policy design because of the fact that the release decision during this period is highly dependent on wet season water conservation and flood risk management operations. Imperatively, the operation logic in the two seasons are quite different. If the two operations are not sufficiently coordinated, they may produce poor responses to the system dynamics. There are high levels of uncertainties on the model parameters, values and how they are logically operated by human or automated systems. Soft computing methods represent the system as an artificial neural network (ANN) in which the input- output relations take the form of fuzzy numbers, fuzzy arithmetic and fuzzy logic (FL). Neuro-Fuzzy System (NFS) soft computing combine the approaches of FL and ANN for single purpose reservoir operation. Thus, this study proposes a Bi-Level Neuro-Fuzzy System (BL-NFS) soft computing methodology for short and long term operation policies for a newly inaugurated irrigation project in Gidabo Watershed of Main Ethiopian Rift Valley Basin. Keywords: Bankruptcy rule, BL-NFS, Reservoir operation, Sensitivity analysis, Soft computing, Water conservation.

Hybrid Fuzzy System Dynamics Model for Analyzing the Impacts of Interrelated Risk and Opportunity Events on Project Contingency

2020 ◽  
Author(s):  
Nasir Bedewi Siraj ◽  
Aminah Robinson Fayek
Keyword(s):  
System Dynamics ◽  
Fuzzy System ◽  
Historical Data ◽  
Subjective Assessment ◽  
Fuzzy Arithmetic ◽  
System Dynamics Model ◽  
Dynamics Model ◽  
Work Package ◽  
Fuzzy Dematel ◽  
Analysis Techniques

Traditional risk analysis techniques are ineffective for capturing the dynamic causal interactions and subjective uncertainties involved in assessing risk and opportunity events since they treat risks independently and rely on the availability of sufficient historical data. In this paper, a hybrid fuzzy system dynamics (FSD) model is developed to analyze the impacts of interrelated and interacting risk and opportunity events on work package cost to determine work package and project contingencies using expert judgement and subjective assessment. A fuzzy DEMATEL method is employed to structure and analyze the causal interactions among risk and opportunity events. This paper provides the following contributions: (1) a systematic risk assessment and prioritization procedure; (2) a structured method for defining the dynamic causal relationships among risk and opportunity events and quantifying their impact on work package and project contingencies using FSD; and (3) a method for representing linguistic variables and applying fuzzy arithmetic in FSD.

A MULTICRITERIA SYSTEM USING GRADUAL FUZZY RULES AND FUZZY ARITHMETIC

2008 ◽  
Vol 16 (supp01) ◽  
pp. 17-34 ◽  
Author(s):  
SANDRA SANDRI ◽  
CHRISTOPHE SIBERTIN-BLANC
Keyword(s):  
Fuzzy System ◽  
Fuzzy Rules ◽  
Fuzzy Arithmetic ◽  
Social Games ◽  
Precise Information ◽  
Similarity Relations ◽  
Multicriteria Problem

We present a multicriteria fuzzy system that uses fuzzy gradual rules bases to model the user's preferences and fuzzy arithmetic to aggregate the results issued by the bases. We first present a multicriteria problem and its solution for the case of precise information. Then we extend the model to treat pieces of information that may involve imprecision/vagueness. We show that the use of residuated implication operators (employed by gradual rules), coupled with similarity relations (to guarantee consistency), offer a better treatment of the problem than a Mamdani-like approach. We illustrate the various features of the system by means of a simple example and describe an application of the formalism to social games.

Fuzzy system F-CalcRank for calculating functions of fuzzy arguments and ranking of fuzzy numbers

2021 ◽  
pp. 1-16
Author(s):  
Alexander Radaev ◽  
Alexander Korobov ◽  
Boris Yatsalo
Keyword(s):  
User Interfaces ◽  
Fuzzy System ◽  
Fuzzy Numbers ◽  
University Education ◽  
Fuzzy Arithmetic ◽  
Membership Functions ◽  
Ranking Methods ◽  
Fuzzy Environment ◽  
System F ◽  
Ranking Of Fuzzy Numbers

Assessing functions of fuzzy arguments and ranking of fuzzy quantities are two key steps in fuzzy modeling and Fuzzy Multicriteria Decision Analysis (FMCDA). Approximate calculations along with the use of centroid index as a defuzzification based ranking methods are a generally accepted approach to applications in the fuzzy environment. This paper presents a novel fuzzy system, F-CalcRank, which is integration of two coupled fuzzy systems: F-Calc (Fuzzy Calculator) and F-Ranking (Fuzzy Ranking). F-Calc allows assessing functions of fuzzy numbers with the use of different approaches: approximate calculations, standard fuzzy arithmetic, and transformation methods. The input values to F-Calc are fuzzy numbers with the following membership functions: triangular and trapezoidal, Gaussian, bell shape, sigmoid, and piece-wise linear continuous or upper semicontinuous membership functions of any complexity, as well as fuzzy linguistic terms of a given term set. F-Ranking system is intended for ranking of a given set of fuzzy numbers, including those, which are inputs and/or outputs of the F-Calc system. F-Ranking includes six ranking methods: three defuzzification based and three pairwise comparison ones. The structure of F-CalcRank as well as input and output information and the user interfaces of both F-Calc and F-Ranking systems, which can also be used independently, are presented. Examples of computing functions of fuzzy arguments and ranking of fuzzy numbers using implemented methods as well as exploring a real case study in agro-ecology with the use of a math model in fuzzy environment are considered. These examples stress the features and novelty of F-CalcRank system as well as presented applied research. The computer modules created within F-CalcRank are a basis for different FMCDA models developed by the authors. F-CalcRank system is intended for university education, research and various applications in engineering and technology.

Modeling of a Variable Speed Drive Using Adaptive Fuzzy System

2016 ◽  
Vol 10 (1) ◽  
pp. 19
Author(s):  
Sekhar J.N. Chandra ◽  
Marutheswar G.V. ◽  
◽  
Keyword(s):  
Fuzzy System ◽  
Variable Speed ◽  
Variable Speed Drive ◽  
Adaptive Fuzzy ◽  
Adaptive Fuzzy System

Penerapan Logika Samar dalam Peramalan Data Runtun Waktu

2011 ◽  
Vol 3 (2) ◽  
pp. 11-15
Author(s):  
Seng Hansun
Keyword(s):  
Time Series ◽  
Fuzzy Logic ◽  
Time Series Analysis ◽  
Fuzzy Sets ◽  
Fuzzy System ◽  
Time Series Data ◽  
Fuzzy Logic System ◽  
Series Data ◽  
Series Analysis ◽  
Logic System

Recently, there are so many soft computing methods been used in time series analysis. One of these methods is fuzzy logic system. In this paper, we will try to implement fuzzy logic system to predict a non-stationary time series data. The data we use here is Mackey-Glass chaotic time series. We also use MATLAB software to predict the time series data, which have been divided into four groups of input-output pairs. These groups then will be used as the input variables of the fuzzy logic system. There are two scenarios been used in this paper, first is by using seven fuzzy sets, and second is by using fifteen fuzzy sets. The result shows that the fuzzy system with fifteen fuzzy sets give a better forecasting result than the fuzzy system with seven fuzzy sets. Index Terms—forecasting, fuzzy logic, Mackey-Glass chaotic, MATLAB, time series analysis

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