scholarly journals Bulanık Mantık ile Kefir Üretiminin Modellenmesi

2014 ◽  
Vol 2 (6) ◽  
pp. 251
Author(s):  
Hüseyin Nail Akgül ◽  
Filiz Yıldız Akgül ◽  
Tuna Doğan
Keyword(s):  
Fuzzy Logic ◽  
Membership Function ◽  
Incubation Time ◽  
Incubation Temperature ◽  
Ph Value ◽  
Fuzzy Rules ◽  
Average Weight ◽  
Membership Functions ◽  
Input Parameters ◽  
The Membership Function

The fermentation is ended with pH 4.6 values in industrial production of kefir. In this study, the incubation temperature, the incubation time and inoculums of culture were chose as variable parameters of kefir. In conventional control systems, the value of pH can be found by trial method. In these systems, if the number of input parameters is greater, the method of trial and error creates a system dependent on the person as well as troublesome. Fuzzy logic can be used in such cases. Modeling studies with this fuzzy logic control are examined in two portions. The first part consists of fuzzy rules and membership functions, while the second part consists of clarify. Kefir incubation temperature between 20 and 25°C, the incubation period between 18 to 22 hours and the inoculum ratio of culture between 1-5% are selected for optimum production conditions. Three separate fuzzy sets (triangular membership function) are used to blur the incubation temperature, the incubation time and the inoculum ratio of culture. Because the membership function numbers belonging to the the input parameters are 3 units, 3x3x3=27 line rule is obtained by multiplying these numbers. The table of fuzzy rules was obtained using the method of Mamdani. The membership function values were determined by the method of average weight using three trapezoidal area of membership functions created for clarification. The success of the system will be found, comparing the numerical values obtained with pH values that should be. Eventually, to achieve the desired pH value of 4.6 in the production of kefir, with the using of fuzzy logic, the workload of people will be decreased and the productivity of business can be increased. In this case, it can be provided savings in both cost and time.

scholarly journals Fuzzy Logic Optimization Method for Energy Efficiency Improvement of CFFS using GA in WSN

2020 ◽  
Vol 9 (6) ◽  
pp. 474-480
Keyword(s):  
Energy Efficiency ◽  
Fuzzy Logic ◽  
Membership Function ◽  
Optimization Method ◽  
Sensor Nodes ◽  
Function Parameter ◽  
Network Environment ◽  
Physical Damage ◽  
False Report ◽  
The Membership Function

Recently, the range of applications for wireless sensor networks has grown. In industrial applications using data-driven approaches, data reliability is particularly important. However, deployed sensor nodes can be easily damaged due to physical damage or node acquisition factors caused by attackers, and false report injection attacks may occur. CFFS with collaborative verification has been proposed to filter out false reports. The proposed CFFS reduces the probability of a successful attack by separating sensor nodes into clusters. The false report filtering performance in the existing scheme is determined according to the pre-security strength setting. Unfortunately, with CFFS, it is impossible to secure each cluster because multiple attacks in a region are not considered. DCFFS uses fuzzy logic to enable security management for each cluster in consideration of the network environment and the geographical arrangement of the nodes. It is necessary for a network administrator to adjust the scope of the membership function parameter to fit the network environment to ensure that the output has an appropriate security strength value for the environment; however, this is difficult to know because it has dissimilar optimum ranges for each application. This paper introduces a fuzzy optimization method that can be adapted to various environments using a genetic algorithm in CFFS. The energy efficiency of nodes is increased by correcting the scope of the membership function in the proposed method. We used experiments to verify that the energy efficiency of the proposed scheme is increased, as compared to the existing scheme.

scholarly journals Formulation of the membership function and determination of the input of fuzzy loads in the structural fuzzy analyzing problem

2007 ◽  
Vol 29 (2) ◽  
pp. 117-126
Author(s):  
Nguyen Van Pho
Keyword(s):  
Membership Function ◽  
Input Parameter ◽  
Design Standards ◽  
Input Parameters ◽  
Triangular Membership Function ◽  
Probabilistic Density Function ◽  
Fuzzy Parameters ◽  
Selection Of ◽  
The Membership Function

The fuzzy analyzing process consists of different steps. In this paper, the author considers only the method for formulation of the membership function of fuzzy loads acting on the structure. Based on the membership function of fuzzy loads, the combinations of deterministic of the regression analyzing process will be determined. The membership function of fuzzy loads is selected by the triangular membership function. It is in conformity with the concept on selection of loads in the design standards. The combination of inputs for the analyzing process will be determined, based on the number of present times of the value of input parameters (including the deterministic parameters, fuzzy parameters and the random ones) in the schema of analysis. The number of present times of input parameters is either proportional to value of the corresponding membership function or to the value of the probabilistic density function. A method for determining the appropriate combination of deterministic inputs so that each input parameter will present only one time in each combination is proposed. To illustrate the proposed method, an example on the determination of input combinations of tornado's velocity in Vietnam is presented.

scholarly journals Learning Membership Functions in a Function-Based Object Recognition System

1995 ◽  
Vol 3 ◽  
pp. 187-222 ◽  
Author(s):  
K. Woods ◽  
D. Cook ◽  
L. Hall ◽  
K. Bowyer ◽  
L. Stark
Keyword(s):  
Object Recognition ◽  
Membership Function ◽  
Learning Algorithm ◽  
Physical Property ◽  
Recognition System ◽  
Tree Structure ◽  
Membership Functions ◽  
Recognition Systems ◽  
Membership Value ◽  
The Membership Function

Functionality-based recognition systems recognize objects at the category level by reasoning about how well the objects support the expected function. Such systems naturally associate a ``measure of goodness'' or ``membership value'' with a recognized object. This measure of goodness is the result of combining individual measures, or membership values, from potentially many primitive evaluations of different properties of the object's shape. A membership function is used to compute the membership value when evaluating a primitive of a particular physical property of an object. In previous versions of a recognition system known as Gruff, the membership function for each of the primitive evaluations was hand-crafted by the system designer. In this paper, we provide a learning component for the Gruff system, called Omlet, that automatically learns membership functions given a set of example objects labeled with their desired category measure. The learning algorithm is generally applicable to any problem in which low-level membership values are combined through an and-or tree structure to give a final overall membership value.

scholarly journals A Generalized Fuzzy Integer Programming Approach for Environmental Management under Uncertainty

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Y. R. Fan ◽  
G. H. Huang ◽  
K. Huang ◽  
L. Jin ◽  
M. Q. Suo
Keyword(s):  
Integer Programming ◽  
Fuzzy Sets ◽  
Membership Function ◽  
Mixed Integer ◽  
Programming Approach ◽  
Management Problem ◽  
Membership Functions ◽  
Interactive Algorithm ◽  
Waste Flow ◽  
The Membership Function

In this study, a generalized fuzzy integer programming (GFIP) method is developed for planning waste allocation and facility expansion under uncertainty. The developed method can (i) deal with uncertainties expressed as fuzzy sets with known membership functions regardless of the shapes (linear or nonlinear) of these membership functions, (ii) allow uncertainties to be directly communicated into the optimization process and the resulting solutions, and (iii) reflect dynamics in terms of waste-flow allocation and facility-capacity expansion. A stepwise interactive algorithm (SIA) is proposed to solve the GFIP problem and generate solutions expressed as fuzzy sets. The procedures of the SIA method include (i) discretizing the membership function grade of fuzzy parameters into a set ofα-cutlevels; (ii) converting the GFIP problem into an inexact mixed-integer linear programming (IMILP) problem under eachα-cut level; (iii) solving the IMILP problem through an interactive algorithm; and (iv) approximating the membership function for decision variables through statistical regression methods. The developed GFIP method is applied to a municipal solid waste (MSW) management problem to facilitate decision making on waste flow allocation and waste-treatment facilities expansion. The results, which are expressed as discrete or continuous fuzzy sets, can help identify desired alternatives for managing MSW under uncertainty.

Optimization of Fuzzy Logic Controller Using PSO for Mobile Robot Navigation in an Unknown Environment

2014 ◽  
Vol 541-542 ◽  
pp. 1053-1061 ◽  
Author(s):  
Mohammed Algabri ◽  
Hedjar Ramdane ◽  
Hassan Mathkour ◽  
Khalid Al-Mutib ◽  
Mansour Alsulaiman
Keyword(s):  
Fuzzy Logic ◽  
Mobile Robot ◽  
Membership Function ◽  
Fuzzy Logic Controller ◽  
Robot Navigation ◽  
Field Performance ◽  
Mobile Robot Navigation ◽  
Autonomous Mobile Robot ◽  
Swarm Optimization ◽  
The Membership Function

The control of autonomous mobile robot in an unknown environments include many challenge. Fuzzy logic controller is one of the useful tool in this field. Performance of fuzzy logic controlling depends on the membership function, so the membership function adjusting is a time consuming process. In this paper, we optimized a fuzzy logic controller (Fuzzy) by automatic adjusting the membership function using a particle swarm optimization (PSO). The proposed method (PSO-Fuzzy) is implemented and compared with Fuzzy using Khepera simulator. Moreover, the performance of these approaches compared through experiments using a real Khepera III platform.

scholarly journals Evaluation of approximate Fuzzy Membership Function using Linguistic Input-an Approached Based on Cubic Spline

2020 ◽  
Vol 1 (2) ◽  
Author(s):  
Stabania Chowdhury ◽  
Rahul Kar
Keyword(s):  
Fuzzy Logic ◽  
Membership Function ◽  
Numerical Data ◽  
Building Blocks ◽  
Cubic Spline ◽  
Triangular Fuzzy Number ◽  
Fuzzy Membership ◽  
Fuzzy Membership Function ◽  
Fuzzy Logic Systems ◽  
The Membership Function

Fuzzy logic systems have found extensive use in system identification, decision making, and pattern recognition problems from industries to academics. The membership functions play a pivotal role in overall role in fuzzy representation, as these are considered as the building blocks of fuzzy set theory and they decide the degree of truth in fuzzy logic. The extraction of the membership function is ambience dependent and thus complication exists in the process of evaluation. In this assessment the main work deals with the derivation of fuzzy membership function where numerical data is available. The numerical cubic spline and defuzzification technique are used here. In this paper we mainly used triangular fuzzy number to construct the membership function. A case study is furnished to emphasize the advantage of adopting the method.

scholarly journals Gaussian Membership Function used for Voice Recognition in Fuzzy Logic

2020 ◽  
Vol 8 (5) ◽  
pp. 2685-2689
Keyword(s):  
Fuzzy Logic ◽  
Membership Function ◽  
Group Membership ◽  
Voice Recognition ◽  
Age Group ◽  
Membership Functions ◽  
Human Voice ◽  
Degree Of Membership ◽  
Membership Value

Gaussian Membership function of a fuzzy set is a generalization form which is used to classify the human voice either based gender or age group. Membership functions were introduced by Zadeh in the first paper on fuzzy sets in the year 1965. In this paper we describe Gaussian membership function which we used to implement the simulation or classification of the human according to their age in fuzzy logic. A Gaussian Membership Function (MF) is a curve that defines how each point in the input space is mapped to a membership value (or degree of membership) between 0 and 1.

scholarly journals Method of Integral Assessment of Soil Quality in Rural-Urban Areas Based on the Fuzzy Logic

2018 ◽  
Vol 7 (4.36) ◽  
pp. 1
Author(s):  
Olga A. Ivashchuk ◽  
Vjacheslav I. Fedorov ◽  
Alexandr V. Koskin ◽  
Natalia V. Shcherbinina ◽  
Maxim D. Zhuravlev
Keyword(s):  
Fuzzy Logic ◽  
Soil Quality ◽  
Membership Function ◽  
Urban Areas ◽  
Fuzzy Inference ◽  
Quality Parameters ◽  
Point Of View ◽  
Linguistic Variables ◽  
Integral Assessment ◽  
The Membership Function

This article proposes a method of integral assessment of soil quality in rural-urban areas based on the fuzzy logic, both from the standpoint of the possibility of living in a given territory, and from the point of view of the possibility of keeping subsidiary farming. There are determined the linguistic variables describing the main soils components of rural-urban areas and terms describing the meaning of these variables. There are constructed the membership function that determine the ratio of the measured soil quality parameters to the terms and the rules of fuzzy inference. The authors developed an algorithm that describes this method.

scholarly journals LOGIKA FUZZY MENENTUKAN JUMLAH PRODUKSI BERDASARKAN PERSEDIAAN DAN JUMLAH PERMINTAAN

2017 ◽  
Vol 3 (2) ◽  
pp. 187-196
Author(s):  
Yusli Yenni
Keyword(s):  
Fuzzy Logic ◽  
Membership Function ◽  
Fuzzy Input ◽  
Accuracy Level ◽  
The Membership Function

The complicated to determine total of product will be production with stock, make the process of decision be slowly. The purpose of this research for implementated fuzzy logic Mamdani method for determine the total of production basis on the total of stock and the total of request at OSI Electronics Batam, Corp and description of accuracy level. The first process analyze the corporation data with total 12 data start from January – December 2015. The data will be processed using MATLAB application with first step is fuzzyfication defene the membership function. There are 2 information as fuzzy input request and stock and will be processed using triangle and trapezoid membership function. Next step is implicated all rules and this research using 26 rules, rule compositions and the last step is defuzzyfication using bisector method. The accuracy using fuzzy logic that was built were  91,67% and error 9,37%.

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