scholarly journals Estimation of Food Security Risk Level UsingZ-Number-Based Fuzzy System

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Rahib H. Abiyev ◽  
Kaan Uyar ◽  
Umit Ilhan ◽  
Elbrus Imanov ◽  
Esmira Abiyeva
Keyword(s):  
Food Security ◽  
Fuzzy Sets ◽  
Fuzzy System ◽  
Risk Level ◽  
Uncertain Information ◽  
Security Risk ◽  
Fuzzy Logic Systems ◽  
Reliability Parameters ◽  
Basic Parameters

Fuzzy logic systems based on If-Then rules are widely used for modelling of the systems characterizing imprecise and uncertain information. These systems are basically based on type-1 fuzzy sets and allow handling the uncertain and imprecise information to some degree in the developed models. Zadeh extended the concept of fuzzy sets and proposedZ-number characterized by two components, constraint and reliability parameters, which are an ordered pair of fuzzy numbers. Here, the first component is used to represent uncertain information, and the second component is used to evaluate the reliability or the confidence in truth.Z-number is an effective approach to solving uncertain problems. In this paper,Z-number-based fuzzy system is proposed for estimation of food security risk level. To construct fuzzy If-Then rules, the basic parameters cereal yield, cereal production, and economic growth affecting food security are selected, and the relationship between these input parameters and risk level are determined through If-Then fuzzy rules. The fuzzy interpolative reasoning is proposed for construction of inference mechanism of aZ-number-based fuzzy system. The designed system is tested using Turkey cereal data for assessing food security risk level and prediction periods of the food supply.

scholarly journals Assessment of Food Security Risk Level Using Type 2 Fuzzy System

2016 ◽  
Vol 102 ◽  
pp. 547-554 ◽  
Author(s):  
Rahib H. Abiyev ◽  
Kaan Uyar ◽  
Umit Ilhan ◽  
Elbrus Imanov
Keyword(s):  
Food Security ◽  
Fuzzy System ◽  
Risk Level ◽  
Security Risk

ON THE CONTROL OF TUMOR GROWTH VIA TYPE-1 AND INTERVAL TYPE-2 FUZZY LOGIC

2015 ◽  
Vol 15 (05) ◽  
pp. 1550083 ◽  
Author(s):  
SHAHRZAD GHOLAMI ◽  
ARIA ALASTY ◽  
HASSAN SALARIEH ◽  
MEHDI HOSSEINIAN-SARAJEHLOU
Keyword(s):  
Fuzzy Logic ◽  
Tumor Growth ◽  
Cancer Cells ◽  
Fuzzy Controller ◽  
Growth Control ◽  
Uncertain Information ◽  
Fuzzy Logic Systems ◽  
Interval Type

This paper deals with growth control of cancer cells population using type-1 and interval type-2 fuzzy logic. A type-1 fuzzy controller is designed in order to reduce the population of cancer cells, adjust the drug dosage in a manner that allows normal cells re-grow in treatment period and maintain the maximum drug delivery rate and plasma concentration of drug in an appropriate range. Two different approaches are studied. One deals with reducing the number of cancer cells without any concern about the rate of decreasing, and the other takes the rate of malignant cells damage into consideration. Due to the fact that uncertainty is an inherent part of real systems and affects controller efficacy, employing new methods of design such as interval type-2 fuzzy logic systems for handling uncertainties may be efficacious. Influence of noise on the system is investigated and the effect of altering free parameters of design is studied. Using an interval type-2 controller can diminish the effects of incomplete and uncertain information about the system, environmental noises, instrumentation errors, etc. Simulation results confirm the effectiveness of the proposed methods on tumor growth control.

scholarly journals FOOD SECURITY RISK LEVEL ASSESSMENT: A FUZZY LOGIC-BASED APPROACH

2013 ◽  
Vol 27 (1) ◽  
pp. 50-61 ◽  
Author(s):  
Muhd Khairulzaman Abdul Kadir ◽  
Evor L. Hines ◽  
Kefaya Qaddoum ◽  
Rosemary Collier ◽  
Elizabeth Dowler ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Food Security ◽  
Risk Level ◽  
Security Risk

Overview of Type-2 Fuzzy Logic Systems

2012 ◽  
Vol 2 (4) ◽  
pp. 1-28 ◽  
Author(s):  
Ahmad Taher Azar
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Fuzzy Set ◽  
Fuzzy Systems ◽  
Degrees Of Freedom ◽  
Fuzzy Logic Systems ◽  
Logic Systems

Fuzzy set theory has been proposed as a means for modeling the vagueness in complex systems. Fuzzy systems usually employ type-1 fuzzy sets, representing uncertainty by numbers in the range [0, 1]. Despite commercial success of fuzzy logic, a type-1 fuzzy set (T1FS) does not capture uncertainty in its manifestations when it arises from vagueness in the shape of the membership function. Such uncertainties need to be depicted by fuzzy sets that have blur boundaries. The imprecise boundaries of a type-2 fuzzy set (T2FS) give rise to truth/membership values that are fuzzy sets in [0], [1], instead of a crisp number. Type-2 fuzzy logic systems (T2FLSs) offer opportunity to model levels of uncertainty which traditional fuzzy logic type1 struggles. This extra dimension gives more degrees of freedom for better representation of uncertainty compared to type-1 fuzzy sets. A type-1 fuzzy logic system (T1FLSs) inference produces a T1FS and the result of defuzzification of the T1FS, a crisp number, whereas a T2FLS inference produces a type-2 fuzzy set, its type-reduced fuzzy set which is a T1FS and the defuzzification of the type-1 fuzzy set. The type-reduced fuzzy set output gives decision-making flexibilities. Thus, FLSs using T2FS provide the capability of handling a higher level of uncertainty and provide a number of missing components that have held back successful deployment of fuzzy systems in decision making.

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

Processing uncertain information in the linear space of fuzzy sets

1991 ◽  
Vol 44 (2) ◽  
pp. 187-198 ◽  
Author(s):  
Wladyslaw Homenda ◽  
Witold Pedrycz
Keyword(s):  
Fuzzy Sets ◽  
Linear Space ◽  
Uncertain Information

An interval type-2 hesitant fuzzy best-worst method

2021 ◽  
pp. 1-28
Author(s):  
Ashraf Norouzi ◽  
Hossein Razavi hajiagha
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Group Decision Making ◽  
Group Decision ◽  
Fuzzy Ahp ◽  
Uncertain Data ◽  
Interval Type ◽  
Best Worst Method

Multi criteria decision-making problems are usually encounter implicit, vague and uncertain data. Interval type-2 fuzzy sets (IT2FS) are widely used to develop various MCDM techniques especially for cases with uncertain linguistic approximation. However, there are few researches that extend IT2FS-based MCDM techniques into qualitative and group decision-making environment. The present study aims to adopt a combination of hesitant and interval type-2 fuzzy sets to develop an extension of Best-Worst method (BWM). The proposed approach provides a flexible and convenient way to depict the experts’ hesitant opinions especially in group decision-making context through a straightforward procedure. The proposed approach is called IT2HF-BWM. Some numerical case studies from literature have been used to provide illustrations about the feasibility and effectiveness of our proposed approach. Besides, a comparative analysis with an interval type-2 fuzzy AHP is carried out to evaluate the results of our proposed approach. In each case, the consistency ratio was calculated to determine the reliability of results. The findings imply that the proposed approach not only provides acceptable results but also outperforms the traditional BWM and its type-1 fuzzy extension.

scholarly journals FPGA Synthesis of SIRM Fuzzy System-Classification Of Diabetic Epilepsy Risk Level

2012 ◽  
Vol 38 ◽  
pp. 391-404
Author(s):  
N.B. Balamurugan ◽  
M. Jothi ◽  
R. Harikumar
Keyword(s):  
Fuzzy System ◽  
Risk Level

Fuzzy System for DOA Estimation of Coherent Signals

2013 ◽  
Vol 427-429 ◽  
pp. 575-581
Author(s):  
Ya Ling Chen ◽  
Chien Chou Lin
Keyword(s):  
Fuzzy System ◽  
High Performance ◽  
Expert Knowledge ◽  
Doa Estimation ◽  
Coherent Signals ◽  
Fuzzy Logic Systems ◽  
Significant Performance ◽  
And Performance ◽  
Low Sensitivity ◽  
Resolution Estimation

This paper presents an efficient direction-of-arrival (DOA) Estimator for dealing with coherent signals. The empirical results show that significant performance degradation occurs when coherent signals coexist. Therefore, an utilizes the low sensitivity of Bartlett algorithm in estimation of DOAs for coherent signals to yield a low-resolution estimation of DOAs as initial search angle and uses fuzzy logic systems with incorporating expert knowledge to improve the resolution and performance of estimation of DOAs in coherent signals environment. Finally, numerical example was analyzed to illustrate high performance of the proposed method and to confirm the designed procedure.

Sign in / Sign up

Export Citation Format

Share Document