Fuzzy logic applied to different adaptability and stability methods in common bean

Abstract: The objective of this work was to evaluate the efficiency of the methods of Eberhart & Russell and Lin & Binns, modified for the automation of decision-making by fuzzy logic, in assessing the adaptability and stability of common bean (Phaseolus vulgaris) cultivars. Eighteen cultivars of the “carioca” commercial group were evaluated in 11 environments, in Brazil. All results were obtained by programming in the R software. The developed controllers were based on the fuzzy inference system developed by Mamdani. This system was modeled to enable interpretations of the method of Eberhart & Russell alone or together with the modified method of Lin & Binns. The controller based on Eberhart & Russell and the one based on Eberhart & Russell and Lin & Binns identified the same cultivars as having general adaptability, but differed as to the classification of cultivars adapted to unfavorable environments. The BRSMG Pioneiro, BRS Pontal, IAC-Carioca Tybatã, and IPR Juriti cultivars presented general adaptability, whereas Campeão, Pérola, and BRS Estilo showed specific adaptability to favorable environments. The fuzzy logic methods used are efficient and allow the classification of all evaluated cultivars.

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Aplikasi Pengambilan Keputusan dengan Metode Tsukamoto pada Penentuan Tingkat Kepuasan pelanggan

CAUCHY ◽

10.18860/ca.v4i1.3168 ◽

2015 ◽

Vol 4 (1) ◽

pp. 10 ◽

Cited By ~ 1

Author(s):

Venny Riana Riana Agustin ◽

Wahyu Henky Irawan

Keyword(s):

Decision Making ◽

Fuzzy Logic ◽

Fuzzy Inference ◽

Weighted Average ◽

Fuzzy Rules ◽

Quality Of Services ◽

Inference System ◽

A Value ◽

Fuzzy Logic Analysis

Tsukamoto method is one method of fuzzy inference system on fuzzy logic for decision making. Steps of the decision making in this method, namely fuzzyfication (process changing the input into kabur), the establishment of fuzzy rules, fuzzy logic analysis, defuzzyfication (affirmation), as well as the conclusion and interpretation of the results. The results from this research are steps of the decision making in Tsukamoto method, namely fuzzyfication (process changing the input into kabur), the establishment of fuzzy rules by the general form IF a is A THEN B is B, fuzzy logic analysis to get alpha in every rule, defuzzyfication (affirmation) by weighted average method, as well as the conclusion and interpretation of the results. On customers at the case, in value of 16 the quality of services, the value of 17 the quality of goods, and value of 16 a price, a value of the results is 45,29063 and the level is low satisfaction

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Analysis of The Impact of Fuzzy Logic Algorithm On Handover Decision in A Cellular Network

International Journal for Innovation Education and Research ◽

10.31686/ijier.vol5.iss5.664 ◽

2017 ◽

Vol 5 (5) ◽

pp. 46-62 ◽

Cited By ~ 1

Author(s):

Emily Teresa Nyambati ◽

Vitalice K. Oduol

Keyword(s):

Decision Making ◽

Fuzzy Logic ◽

Intelligent Systems ◽

Fuzzy Inference ◽

Inference System ◽

Handover Decision ◽

Fuzzy Logic Algorithm ◽

Gsm Network ◽

Work Done ◽

The Impact

Fuzzy logic is one of the intelligent systems that can be used to develop algorithms for handover. For success in handing over, the decision-making process is crucial and thus should be highly considered. The performance of fixed parameters is not okay in the changing cellular system environments. The work done on this paper aims to analyse the impact of utilising the fuzzy logic system for handover decision making considering the Global System for Mobile communication (GSM) network. The results from the different simulations show that the need to handover varies depending on the input(s) to the Fuzzy Inference System (FIS). By increasing the number of data, thus the criteria parameters used in the algorithm, an Optimised Handover Decision (OHOD) is realised.

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Fuzzy Logic Modeling for Decision Making Processes Using MATLAB

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.984-985.425 ◽

2014 ◽

Vol 984-985 ◽

pp. 425-430 ◽

Cited By ~ 2

Author(s):

Thangavel Ramya ◽

A.C. Kannan ◽

R.S. Balasenthil ◽

B. Anusuya Bagirathi

Keyword(s):

Decision Making ◽

Fuzzy Logic ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Decision Making Process ◽

Computing Environment ◽

Output Variable ◽

Inference System ◽

Logic Modeling ◽

Decision Making Processes

— This paper demonstrates to build a Fuzzy Inference System (FIS) for any model utilizing the Fuzzy Logic Toolbox graphical user interface (GUI) tools. A different conception for decision making process, based on the fuzzy approach, is propounded by authors of the paper.The paper is worked out in two sections. Description about the Fuzzy Logic Tool box is done in the first section.Illustration with an introductory example concludes the second section. Based on various assumptions the authors construct the rule statements which are then converted into fuzzy rules and the GUI tools of the Fuzzy Logic Toolbox built using MATLAB numeric computing environment is used to construct a fuzzy inference system for this process.The output membership functions are expected to be fuzzy sets in Mamdani-type inference.Defuzzification of fuzzy set for each output variable generated after the aggregation process has to be carried out. Application of information technology for Decisions in today's environment which is highly competitive are undeniable principles of organizations and helps managers in making useful decisions meaningfully.

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Sistem Pendukung Keputusan Metode Sugeno dalam Menentukan Tingkat Kepribadian Siswa Berdasarkan Pendidikan

CAUCHY ◽

10.18860/ca.v4i1.3174 ◽

2015 ◽

Vol 4 (1) ◽

pp. 48

Author(s):

Wildan Hakim ◽

Turmudi Turmudi

Keyword(s):

Decision Making ◽

Fuzzy Logic ◽

Fuzzy Inference System ◽

Informal Education ◽

Formal Education ◽

Fuzzy Inference ◽

Zero Order ◽

Composition Rule ◽

Inference System ◽

A Value

Sugeno method is one method of fuzzy inference system on fuzzy logic for decision making. In-Zero Order Sugeno method in fuzzy logic consists of four stages: 1. fuzzification. 2. The application functionality implications, the implication function used is function MIN (minimum). 3. The composition rule using the function MAX (maximum). 4. Defuzzification weight average. Based on case 1, each student with non-formal education and informal education by 12 by 19 has a value of 20.7 and a variable linguistic personality is MEDIUM. Suggestions for further research can use other parameters in determining the level of personality of students with fuzzy logic

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Vessel Multi-Parametric Collision Avoidance Decision Model: Fuzzy Approach

Journal of Marine Science and Engineering ◽

10.3390/jmse9010049 ◽

2021 ◽

Vol 9 (1) ◽

pp. 49

Author(s):

Tanja Brcko ◽

Andrej Androjna ◽

Jure Srše ◽

Renata Boć

Keyword(s):

Fuzzy Logic ◽

Collision Avoidance ◽

Decision Model ◽

Fuzzy Inference ◽

Weather Conditions ◽

Target Vessel ◽

Planning Tool ◽

Inference System ◽

International Regulations ◽

Input Variables

The application of fuzzy logic is an effective approach to a variety of circumstances, including solutions to maritime anti-collision problems. The article presents an upgrade of the radar navigation system, in particular, its collision avoidance planning tool, using a decision model that combines dynamic parameters into one decision—the collision avoidance course. In this paper, a multi-parametric decision model based on fuzzy logic is proposed. The model calculates course alteration in a collision avoidance situation. First, the model collects input data of the target vessel and assesses the collision risk. Using time delay, four parameters are calculated for further processing as input variables for a fuzzy inference system. Then, the fuzzy logic method is used to calculate the course alteration, which considers the vessel’s safety domain and International Regulations for Preventing Collisions at Sea (COLREGs). The special feature of the decision model is its tuning with the results of the database of correct solutions obtained with the manual radar plotting method. The validation was carried out with six selected cases simulating encounters with the target vessel in the open sea from different angles and at any visibility. The results of the case studies have shown that the decision model computes well in situations where the own vessel is in a give-way position. In addition, the model provides good results in situations when the target vessel violates COLREG rules. The collision avoidance planning tool can be automated and serve as a basis for further implementation of a model that considers the manoeuvrability of the vessels, weather conditions, and multi-vessel encounter situations.

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Adaptive Neuro-Fuzzy Inference System for Classification of ACL-Ruptured Knees Using Arthrometric Data

Annals of Biomedical Engineering ◽

10.1007/s10439-008-9532-x ◽

2008 ◽

Vol 36 (9) ◽

pp. 1449-1457 ◽

Cited By ~ 8

Author(s):

Zoya Heydari ◽

Farzam Farahmand ◽

Hossein Arabalibeik ◽

Mohamad Parnianpour

Keyword(s):

Fuzzy Inference System ◽

Fuzzy Inference ◽

Inference System ◽

Neuro Fuzzy

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A Soft Computing Approach to Crack Detection and Impact Source Identification with Field-Programmable Gate Array Implementation

Advances in Fuzzy Systems ◽

10.1155/2013/343174 ◽

2013 ◽

Vol 2013 ◽

pp. 1-12 ◽

Cited By ~ 2

Author(s):

Arati M. Dixit ◽

Harpreet Singh

Keyword(s):

Fuzzy Logic ◽

Soft Computing ◽

Fuzzy Inference System ◽

Source Identification ◽

Field Programmable Gate Array ◽

Crack Detection ◽

Fuzzy Inference ◽

Inference System ◽

Field Programmable ◽

Computing Approach

The real-time nondestructive testing (NDT) for crack detection and impact source identification (CDISI) has attracted the researchers from diverse areas. This is apparent from the current work in the literature. CDISI has usually been performed by visual assessment of waveforms generated by a standard data acquisition system. In this paper we suggest an automation of CDISI for metal armor plates using a soft computing approach by developing a fuzzy inference system to effectively deal with this problem. It is also advantageous to develop a chip that can contribute towards real time CDISI. The objective of this paper is to report on efforts to develop an automated CDISI procedure and to formulate a technique such that the proposed method can be easily implemented on a chip. The CDISI fuzzy inference system is developed using MATLAB’s fuzzy logic toolbox. A VLSI circuit for CDISI is developed on basis of fuzzy logic model using Verilog, a hardware description language (HDL). The Xilinx ISE WebPACK9.1i is used for design, synthesis, implementation, and verification. The CDISI field-programmable gate array (FPGA) implementation is done using Xilinx’s Spartan 3 FPGA. SynaptiCAD’s Verilog Simulators—VeriLogger PRO and ModelSim—are used as the software simulation and debug environment.

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