scholarly journals Algorithm Apriori Association Rule in Determination of Fuzzy Rule Based on Comparison of Fuzzy Inference System (FIS) Mamdani Method and Sugeno Method

2016 ◽  
Vol 7 (1) ◽  
pp. 103
Author(s):  
Muhammad Fadli Arif ◽  
Bima Anoraga ◽  
Samingun Handoyo ◽  
Harisaweni Nasir
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Expert Knowledge ◽  
Fuzzy Rule ◽  
Percentage Error ◽  
Inference System ◽  
Rule Based ◽  
Accuracy Measurement ◽  
First Order

<p>The economic stability of a country can be determined from the changes in the rate of inflation. Inflation is measured by the annual percentage change in the Consumer Price Index. Since there exists some uncertainties in the inflation data, fuzzy logic is one of the ways to analyse the data. Decisions in fuzzy logic can be made using the fuzzy rule-based inference system. Fuzzy rule-based inference can be obtained from expert knowledge, but the knowledge from the experts on the working of a system is not always available. Therefore, the use of association rules<em> </em>approach could solve the problem. Using three methods of fuzzy inferences; namely the Mamdani Methods, zero-order Sugeno method, and the first-order Sugeno method, this study was carried out to determine which method fits to predict the general monthly inflation data in Indonesia. The Inflation data were derived from the inflation of foodstuff price, <em>X<sub>1</sub></em>; inflation of food, drinks, cigarettes and tobacco prices, <em>X<sub>2</sub></em>; inflation of housing, water, electricity, gas, and fuel prices, <em>X<sub>3</sub></em>; inflation of clothing price, <em>X<sub>4</sub></em>; inflation of health care price, <em>X<sub>5</sub></em>; inflation of education, recreation, and sports prices, <em>X<sub>6</sub></em>; and inflation of transportation, communication, and financial services prices, <em>X<sub>7</sub></em>. The performance of the three methods was compared using mean squared error (MSE) and mean absolute percentage error (MAPE) as the accuracy measurement to establish the best fuzzy inference method that fits the inflation value. It was found that the most appropriate method which generated the most accurate results to fit the fuzzy inference system to the inflation data was the first-order Sugeno method.</p>

scholarly journals Rain prediction using fuzzy rule based system in North-West malaysia

2019 ◽  
Vol 14 (3) ◽  
pp. 1564
Author(s):  
Noor Zuraidin Mohd Safar ◽  
Azizul Azhar Ramli ◽  
Hirulnizam Mahdin ◽  
David Ndzi ◽  
Ku Muhammad Naim Ku Khalif
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Expert Knowledge ◽  
Fuzzy Rule ◽  
Complex Problem ◽  
Dew Point ◽  
Inference System ◽  
North West ◽  
Rainfall Prediction

<span>The warm and humid condition is the characteristic of Malaysia tropical climate. Prediction of rain occurrences is important for the daily operations and decisions for the country that rely on agriculture needs. However predicting rainfall is a complex problem because it is effected by the dynamic nature of the tropical weather parameters of atmospheric pressure, temperature, humidity, dew point and wind speed. Those parameters have been used in this study. The rainfall prediction are compared and analyzed.   Fuzzy Logic and Fuzzy Inference System can deal with ambiguity that often occurred in meteorological prediction; it can easily incorporate with expert knowledge and empirical study into standard mathematical. This paper will determine the dependability of Fuzzy Logic approach in rainfall prediction within the given approximation of rainfall rate, exploring the use of Fuzzy Logic and to develop the fuzzified model for rainfall prediction. The accuracy of the proposed Fuzzy Inference System model yields 72%</span>

scholarly journals Genetic fuzzy system for prediction of respiratory rate of chicks subject to thermal challenges

2018 ◽  
Vol 22 (6) ◽  
pp. 412-417 ◽  
Author(s):  
Patrícia F. P. Ferraz ◽  
Tadayuki Yanagi Junior ◽  
Yamid F. Hernandez-Julio ◽  
Gabriel A. e S. Ferraz ◽  
Maria A. J. G. Silva ◽  
...  
Keyword(s):  
Respiratory Rate ◽  
Fuzzy Inference System ◽  
Prediction Accuracy ◽  
Fuzzy Inference ◽  
Fuzzy Rule ◽  
System Model ◽  
Inference System ◽  
Rule Based ◽  
Rule Based System ◽  
Genetic Fuzzy System

ABSTRACT The aim of this study was to estimate and compare the respiratory rate (breath min-1) of broiler chicks subjected to different heat intensities and exposure durations for the first week of life using a Fuzzy Inference System and a Genetic Fuzzy Rule Based System. The experiment was conducted in four environmentally controlled wind tunnels and using 210 chicks. The Fuzzy Inference System was structured based on two input variables: duration of thermal exposure (in days) and dry bulb temperature (°C), and the output variable was respiratory rate. The Genetic Fuzzy Rule Based System set the parameters of input and output variables of the Fuzzy Inference System model in order to increase the prediction accuracy of the respiratory rate values. The two systems (Fuzzy Inference System and Genetic Fuzzy Rule Based System) proved to be able to predict the respiratory rate of chicks. The Genetic Fuzzy Rule Based System interacted well with the Fuzzy Inference System model previously developed showing an improvement in the respiratory rate prediction accuracy. The Fuzzy Inference System had mean percentage error of 2.77, and for Fuzzy Inference System and Genetic Fuzzy Rule Based System it was 0.87, thus indicating an improvement in the accuracy of prediction of respiratory rate when using the tool of genetic algorithms.

scholarly journals PREDIKSI HASIL PANEN PADI KABUPATEN & KOTA DI PROPINSI NUSA TENGGARA TIMUR DENGAN FUZZY INFERENCE SYSTEM (FIS)

2018 ◽  
Vol 10 (1) ◽  
pp. 42-48
Author(s):  
Yampi R. Kaesmetan
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference System ◽  
Agricultural Sector ◽  
Crop Yields ◽  
Fuzzy Inference ◽  
Rice Production ◽  
Percentage Error ◽  
Centroid Method ◽  
Inference System ◽  
Rice Yields

Rice (Oryza sativa) is a staple food source for the people of Indonesia. Most of the rice consumed is the result of national rice productivity. Often the government has difficulty in estimating the adequacy of basic food items that can be provided by domestic agriculture. Therefore a method is needed to predict rice yields accurately and precisely. The agricultural sector in East Nusa Tenggara is not a flagship of the community's economic activities. This is due to the geographical conditions of NTT which are less supportive for business activities in the agricultural sector. Even so, the prediction of agricultural products, especially rice yields, is needed to be predicted so that a forecast can be obtained in determining rice yields in 2017.  Fuzzy logic method in this case Fuzzy Inference System (FIS) is widely applied for forecasting or prediction. Fuzzy logic has a slowness in predicting crop yields for the following year based on crop yields in the previous year and information taken from the fuzzy information provided. Fuzzyinformation can be made a rule or rule as a consideration in predicting yields. By using the formula of Mean Absolute Percentage Error (MAPE) or Average Absolute Error, from the Fuzzy Mamdani model The Fuzzy Inference System (FIS) with the Mamdani model that has been built can be used to estimate the amount of rice production in the City District in NTT with the truth value reaching 97.8%. To determine the amount of rice production in 2017, the data is processed by using the help of the Matlab 2012 fuzzy toolbox software using the centroid method for defuzzification.

scholarly journals KNOWLEDGE-BASED FIS AND ANFIS MODELS DEVELOPMENT AND COMPARISON FOR RESIDENTIAL REAL ESTATE VALUATION

2018 ◽  
Vol 22 (2) ◽  
pp. 110-118 ◽  
Author(s):  
Sukran YALPIR ◽  
Gulgun OZKAN
Keyword(s):  
Real Estate ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Expert Knowledge ◽  
Percentage Error ◽  
Residential Real Estate ◽  
Inference System ◽  
Market Values ◽  
Knowledge Based ◽  
Real Estate Valuation

There has been an increasing concern on the development of alternative approaches to overcome the problems and deficiencies that occur during the application of real-estate valuation methods. This study was established to investigate the usability of the expert knowledge based fuzzy logic methodology in determining real-estates values. In addition, valuation with the Adaptive Neuro-Fuzzy Inference System (ANFIS) method provided model comparison. Samples were administered a questionnaire for the parameters planned for these models regarding the parameters that affect real estate values. To make value estimations for the Fuzzy Inference System (FIS) model by using the parameters obtained from the questionnaire analyses, the criteria that produced the best results were acquired from the various criteria alternatives. An algorithm was created and the valuation process for real estate was performed using the FIS in Konya/Turkey. As a result of poll studies the area, age, floor conditions, physical properties and location of the real-estate property were considered as the input variables and the market value as the output variable. The memberships were established with poll analysis and were rule based on expert knowledge. The model structure was formed by using the Mamdani structure in the MATLAB fuzzy toolbox. Model prediction performance was evaluated statistically with the Mean Absolute Percentage Error (MAPE) and a high accuracy of the model results to the market values indicated the reliability of the established model for residential real-estate valuation.

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

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
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.

scholarly journals A Fuzzy Logic Based System for Secure Scrum

2019 ◽  
Vol 8 (4) ◽  
pp. 8961-8964
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference System ◽  
System Performance ◽  
Fuzzy Inference ◽  
System Model ◽  
Basic System ◽  
Inference System ◽  
General Functioning ◽  
Software Engineers

Software is a basic system that acts as a major key part in general functioning system like securing the need of performance and scope of the system. Here the security is given to unauthorized user as unauthorized client that casually gets the change or modification within the system by effecting the efficiency and functionality of the system. So in order to overcome this issue new improved software is taken that improves the system performance and security. the paper represents a new fuzzy logic based system for handling secured attribute and assessment in software. Based on this reason we propose PC1 and bugs dataset for fuzzy inference system can be used. This secured system model helps software engineers to select secured and safety software for the performance and ambiguity.

Rainfall events prediction using rule-based fuzzy inference system

2011 ◽  
Vol 101 (1-2) ◽  
pp. 228-236 ◽  
Author(s):  
Somia A. Asklany ◽  
Khaled Elhelow ◽  
I.K. Youssef ◽  
M. Abd El-wahab
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Rule Based ◽  
Rainfall Events

EKSTRAKSI FITUR PEMBULUH DARAH CITRA FUNDUS RETINA MENGGUNAKAN FUZZY LOGIC

2021 ◽  
Vol 26 (2) ◽  
pp. 163-175
Author(s):  
Asyaroh Ramadona Nilawati ◽  
Taufik Hidayat
Keyword(s):  
Fuzzy Logic ◽  
Optic Disc ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Median Filtering ◽  
Inference System

Ekstraksi pola pembuluh darah retina dapat dimanfaatkan dalam sistem biometrik sebagai otentikasi keamanan. Citra hasil ekstraksi pola pembuluh darah retina dapat dimasukkan ke dalam fitur untuk identifikasi sistem biometrik. Salah satu metode yang dapat dilakukan untuk melakukan segmentasi pembuluh darah retina adalah metode fuzzy logic. Pada penelitian ini, dilakukan ekstraksi pembuluh darah citra fundus retina menggunakan implementasi fuzzy logic. Peneliti menggunakan sejumlah 20 citra fundus yang diperoleh dari dataset DRIVE berformat .tif. Proses segmentasi dimulai dengan tahap preprocessing yang berisikan konversi citra menjadi grayscale, median filtering, perataan histogram CLAHE, dan eliminasi optic disc, kemudian dilanjutkan dengan pembuatan fuzzy inference system. Tahapan preprocessing yang digunakan merupakan hasil dari rangkaian uji coba peneliti dengan melihat hasil dari setiap uji coba yang dilakukan, sehingga mendapatkan citra yang menonjolkan fitur pembuluh darah dan menghilangkan noise atau fitur retina yang tidak diperlukan seperti optic disc. Uji coba segmentasi dilakukan pada Polyspace R2020a sebagai media untuk menjalankan program mulai dari preprocessing hingga segmentasi menggunakan fuzzy logic. Keluaran dari segmentasi ini berupa citra segmentasi hasil dari metode fuzzy logic dan crisp value. Metode fuzzy logic berhasil diterapkan untuk melakukan ekstraksi pembuluh darah retina dan menghasilkan crisp value. Hasil penelitian ini diharapkan dapat digunakan sebagai salah satu fitur sistem identifikasi biometrik retina.

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