Academic literature on the topic 'Mamdani fuzzy model'
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Journal articles on the topic "Mamdani fuzzy model"
1
Agus, Nursikuwagus, and Baswara Agis. "A Mamdani Fuzzy Model to Choose Eligible Student Entry." TELKOMNIKA Telecommunication, Computing, Electronics and Control 15, no. 1 (2017): 365–72. https://doi.org/10.12928/TELKOMNIKA.v15i1.4893.
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This paper presented about study that have been created a new student choosing system by using fuzzy mamdani inference systems method. Fuzzy mamdani is used because it has characteristics such as human perceptions on choosing of students with some specified criteria. The choosing students who want entry to the school have been difficult if it is manually process. With the fuzzy mamdani, the process can be possible completed execute and can be reduced the time of choose. To accomplish the process, the fuzzy variable is created by the national final exam scores, report grade, general competency test, physical test, interview and psychological test. Based on testing 270 data, the fuzzy mamdani has been reached 75.63% accuracy.
2
Agus, Nursikuwagus, and Baswara Agis. "A Mamdani Fuzzy Model to Choose Eligible Student Entry." TELKOMNIKA Telecommunication, Computing, Electronics and Control 15, no. 1 (2017): 365–72. https://doi.org/10.12928/TELKOMNIKA.v15i1.5099.
Full textAbstract:
This paper presented about study that have been created a new student choosing system by using fuzzy mamdani inference systems method. Fuzzy mamdani is used because it has characteristics such as human perceptions on choosing of students with some specified criteria. The choosing students who want entry to the school have been difficult if it is manually process. With the fuzzy mamdani, the process can be possible completed execute and can be reduced the time of choose. To accomplish the process, the fuzzy variable is created by the national final exam scores, report grade, general competency test, physical test, interview and psychological test. Based on testing 270 data, the fuzzy mamdani has been reached 75.63% accuracy.
3
Harliana, Putri, Mardiana Mardiana, and Yuris Agustira Nainggolan. "Analisa Perbandingan Tingkat Akurasi dalam Memprediksi Laju Inflasi Kota Medan Menggunakan Model Fuzzy Inference System Sugeno dan Mamdani." Hello World Jurnal Ilmu Komputer 1, no. 3 (2022): 145–52. http://dx.doi.org/10.56211/helloworld.v1i3.130.
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Logika fuzzy merupakan perluasan dari penalaran tradisional, di mana x adalah salah satu anggota dari himpunan A atau tidak, atau sebuah x dapat menjadi anggota himpunan A dengan derajat keanggotaan (μ) tertentu. Kemampuan model fuzzy dalam memetakan nilai kabur menjadi alasan penggunaan model inferensi fuzzy dalam berbagai kasus yang menggunakan nilai kabur untuk menghasilkan suatu output yang jelas atau pasti. Dalam penelitian ini akan dilakukan analis tingkat akurasi yang dihasilkan model inferensi fuzzy Sugeno dan Mamdani dalam memprediksi laju inflasi di Kota Medan, hasil prediksi akan dianalisis tingkat akurasinya dengan membandingkan hasil yang diperoleh model fuzzy inferensi Sugeno dan Mamdani dengan nilai aktualnya. Hasil dari analisis yang dilakukan untuk model fuzzy Sugeno tingkat akurasi dipengaruhi nilai regresi linier berganda sedangkan tingkat akurasi dari model fuzzy inferensi Mamdani dipengaruhi oleh ketepatan nilai masukannya. Hasil akhirnya model fuzzy Mamdani lebih akurat dibandingkan dengan model fuzzy inferensi Sugeno dalam uji kasus laju inflasi di Kota Medan.
4
Surorejo, Sarif, Ahadan Fauzan Mutaqin, Rifki Dwi Kurniawan, and Gunawan Gunawan. "Implementation of fuzzy mamdani method in predicting cayenne chili prices in Tegal Regency." Journal of Intelligent Decision Support System (IDSS) 7, no. 2 (2024): 137–45. http://dx.doi.org/10.35335/idss.v7i2.231.
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This study investigates the application of Fuzzy Mamdani's method in predicting the price of cayenne pepper in Tegal Regency, one of the important agricultural commodities that has significant economic implications. This study aims to develop an accurate and reliable cayenne pepper price prediction model in Tegal Regency using the fuzzy Mamdani method. Research methods include collecting historical data on cayenne pepper prices, cayenne pepper production, and rainfall, as well as the implementation of the Mamdani fuzzy method consisting of fuzzification, inference, and defuzzification using Python programming language computing. The results showed that the fuzzy Mamdani method can predict the price of cayenne pepper with a good level of accuracy, with an average prediction error of 16.653285% and a prediction correctness rate of 83.346715%. This finding has implications for improving production planning capabilities and marketing strategies for cayenne pepper farmers in Tegal District, as well as contributing to the scientific literature in the application of fuzzy methods in agriculture
5
Liutkevičius, R. "Fuzzy Hammerstein Model of Nonlinear Plant." Nonlinear Analysis: Modelling and Control 13, no. 2 (2008): 201–12. http://dx.doi.org/10.15388/na.2008.13.2.14580.
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This paper presents the synthesis and analysis of the enhanced predictive fuzzy Hammerstein model of the water tank system. Fuzzy Hammerstein model was compared with three other fuzzy models: the first was synthesized using Mamdani type rule base, the second – Takagi-Sugeno type rule base and the third – composed of Mamdani and Takagi-Sugeno rule bases. The synthesized model is invertible so it can be used in the model based control. The fuzzy Hammerstein model was synthesized to eliminate disadvantages of the other fuzzy models. The advantage of the fuzzy Hammerstein model was experimentally proved and presented in this paper.
6
Ardi, Yuan, Syahril Effendi, and Erna Budhiarti Nababan. "Mamdani and Sugeno Fuzzy Performance Analysis on Rainfall Prediction." Randwick International of Social Science Journal 2, no. 2 (2021): 176–92. http://dx.doi.org/10.47175/rissj.v2i2.240.
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Fuzzy logic is an extension of traditional reasoning, where x is a member of set A or not, or an x can be a member of set A with a certain degree of membership . The ability of fuzzy models to map fuzzy values is the reason for using fuzzy inference models in various cases that use fuzzy values to produce a clear or definite output. In this research, an analysis of the level of accuracy generated by the Sugeno and Mamdani inference model will be carried out in predicting rainfall at Polonia Station, Medan, North Sumatra. Prediction results will be analyzed for accuracy by comparing the results obtained by Sugeno fuzzy inference models and Mamdani using Mean Absolute Percent Error (MAPE). When compared to the results of the Mean Absolute Percent Error (MAPE) Sugeno fuzzy inference model of 1.33% and mamdani fuzzy inference model of 1.45%, then the accuracy rate for the Sugeno inference model is 100%-1.33% = 98.67% while the Mamdani fuzzy inference model is 100%-1.45 = 98.55%. The end result is that the membership function model used in the Sugeno fuzzy model is more accurate than the Mamdani fuzzy inference model in the test case of rainfall prediction at Polonia station in Medan. North Sumatra. The results of the analysis carried out for the Sugeno and Mamdani fuzzy models are influenced by the accuracy of the input values. Rainfall prediction is an important thing to study, weather conditions in certain areas can be predicted so that it can help people's daily activities, can determine a series of community social activities. For example, information on rainfall and its classification is widely used as a guide for agriculture, tourism and transportation, for example: Cropping Patterns, Harvest Predictions, Shipping and flight schedules
7
Putra, Dwipa Junika, Nofriadi Nofriadi, and Erlinda Erlinda. "Implementation of Fuzzy Logic Using Mamdani Method to Determine The Quantity of Bag Production (Case Study In Roman Indah Padang Bag Factory)." JURNAL TEKNOLOGI DAN OPEN SOURCE 5, no. 1 (2022): 1–7. http://dx.doi.org/10.36378/jtos.v5i1.2220.
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Roman Indah bag Factory is a bag manufacturing company that receives orders from customers every month. Fuzzy Logic is used to define the production known so far, so that the preferred result can be used as a basis for manager's decision. In the calculation process, Fuzzy Logic Mamdani requires maximum and minimum production data, maximum and minimum demand data, and maximum and minimum inventory data. Fuzzy logic is able to map an input into an output without factor factors. Fuzzy logic is used to create a model of a system that is able to determine the quantity of production. the factors that affect the quantity of production. Fuzzy logic is called the old new logic because the science of modern fuzzy logic and methodological was discovered only a few years ago, in fact the concept of fuzzy logic itself has been in us for a long time. Mamdani method is the most common method when it comes to fuzzy methodology. Mamdani method uses a set of IF-THEN rules derived from experienced operators/experts. The Mamdani model is often known as the Max-Min model. By using Mamdani method in Roman Indah handbag factory can assist in the efficiency of time and labour, because using Mamdani method can calculate the amount of production in the next month, so from the results can be derived consideration material decision by the manager, whether in determining raw materials, promotion, bag model, consumer, HR, etc. so that more company profits.
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Amrul Hinung Prihamayu. "Prediction Of Closing Price Combined Stock Index (Ihsg) Using The Fuzzy Mamdani Method." SOUTHEAST ASIA JOURNAL oF GRADUATE OF ISLAMIC BUSINESS AND ECONOMICS 1, no. 2 (2022): 74–79. http://dx.doi.org/10.37567/sajgibe.v1i2.1862.
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This study proposes a method for predicting the closing IHSG stock price using the Mamdani fuzzy approach. This model uses historical closing stock price data as input, and generates closing stock price predictions using the Mamdani fuzzy rule. However, experimental results show that this model may not be suitable for predicting stock prices accurately and reliably. Therefore, this study does not recommend the use of the Mamdani fuzzy method for the purpose of predicting closing stock prices.
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Dian Eko Hari Purnomo. "PERANCANGAN MODEL ARTIFICIAL INTELLIGENCE (AI) UNTUK MEMBANTU MENENTUKAN PERSEDIAAN BAHAN BAKU KAYU PADA INDUSTRI FURNITUR DENGAN PENDEKATAN METODE FUZZY MAMDANI." Industry Xplore 9, no. 1 (2024): 323–30. https://doi.org/10.36805/teknikindustri.v9i1.6054.
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Perencanaan persedian dalam suatu industri manufaktur tentunya menjadi bagian yang cukup penting. Hal ini dikarenakan jika bahan baku tidak ada maka artinya proses produksi pada industri manufaktur akan terhambat atau mengalami kendala. Sedangkan jika jumlah ketersediaan bahan baku terlalu berlebihan maka akan menimbulkan biaya tambahan. Sehingga untuk dapat melakukan perencanaan persediaan yang sesuai kebutuhan khususnya pada industri manufaktur yang berupa industri furnitur yang bahan baku utamanya berupa kayu. Penelitian ini berusahan melakukan perencanaan persediaan yang optimal dengan menggunakan pendekatan Logika Fuzzy. Pendekatan Fuzzy yang diadopsi atau yang dimplementasikan dalam penelitian ini adalah berupa Metode Fuzzy Mamdani. Alasan dalam penelitian ini menggunakan Metode Fuzzy Mamdani untuk melakukan proses prediksi persediaan kayu pada industri furnitur karena memiliki struktur yang sederhana dan fleksibel. Dalam mekanisme proses perhitungan Metode Fuzzy Mamdani pada umumnya menggunakan konsep operasi yang berupa mekanismer perhitungan min-max ataupun dapat menggunakan mekanisme max-product dengan menggunakan serangkaian aturan yang telah ditentukan berdasarkan variabel yang digunakan. Data yang digunakan pada penelitian ini adalah tahun 2019, 2020, 2021, dan 2022. Dalam proses penyelesaian masalah yang berupa penentuan jumlah persediaan yang optimal adalah dengan menggunakan proses penyelesaian Metode Fuzzy Mamdani. Dari hasil perhitungan menggunakan metode Fuzzy Mamdani. Dengan menggunakan data pada bulan Januari 2022 yang berupa data permintaan yang mempunyai nilai 1711 kubik dan data produksi yang mempunyai nilai 1469 kubik, maka didapatkan hasil pengolahan data dengan Metode Fuzzy Mamdani untuk jumlah persediaan pada periode bulan Januari 2022 adalah senilai 208 kubik.
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Meng, Lei, Shoulin Yin, and Xinyuan Hu. "An improved Mamdani Fuzzy Neural Networks Based on PSO Algorithm and New Parameter Optimization." Indonesian Journal of Electrical Engineering and Computer Science 1, no. 1 (2016): 201. http://dx.doi.org/10.11591/ijeecs.v1.i1.pp201-206.
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As we all know, the parameter optimization of Mamdani model has a defect of easily falling into local optimum. To solve this problem, we propose a new algorithm by constructing Mamdani Fuzzy neural networks. This new scheme uses fuzzy clustering based on particle swarm optimization(PSO) algorithm to determine initial parameter of Mamdani Fuzzy neural networks. Then it adopts PSO algorithm to optimize model's parameters. At the end, we use gradient descent method to make a further optimization for parameters. Therefore, we can realize the automatic adjustment, modification and perfection under the fuzzy rule. The experimental results show that the new algorithm improves the approximation ability of Mamdani Fuzzy neural networks.
Dissertations / Theses on the topic "Mamdani fuzzy model"
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BEZERRA, ROSINI ANTONIO MONTEIRO. "HIERARCHICAL NEURO-FUZZY BSP-MAMDANI MODEL." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2002. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=3129@1.
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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>Esta dissertação investiga a utilização de sistemas Neuro-
Fuzzy Hierárquicos BSP (Binary Space Partitioning) para
aplicações em classificação de padrões, previsão, sistemas
de controle e extração de regras fuzzy. O objetivo é criar
um modelo Neuro-Fuzzy Hierárquico BSP do tipo Mamdani a
partir do modelo Neuro-Fuzzy Hierárquico BSP Class
(NFHB-Class) que é capaz de criar a sua própria estrutura
automaticamente e extrair conhecimento de uma base de dados
através de regras fuzzy, lingüisticamente interpretáveis,
que explicam a estrutura dos dados. Esta dissertação
consiste de quatros etapas principais: estudo dos principais
sistemas hierárquicos; análise do sistema Neuro-Fuzzy
Hierárquico BSP Class, definição e implementação do modelo
NFHB-Mamdani e estudo de casos. No estudo dos principais
sistemas hierárquicos é efetuado um levantamento
bibliográfico na área. São investigados, também, os
principais modelos neuro-fuzzy utilizados em sistemas de
controle - Falcon e o Nefcon. Na análise do sistema NFHB-
Class, é verificado o aprendizado da estrutura, o
particionamento recursivo, a possibilidade de se ter um
maior número de entrada - em comparação com outros sistemas
neuro-fuzzy - e regras fuzzy recursivas. O sistema NFHB-
Class é um modelo desenvolvido especificamente para
classificação de padrões, como possui várias saídas, não é
possível utilizá-lo em aplicações em controle e em
previsão. Para suprir esta deficiência, é criado um novo
modelo que contém uma única saída. Na terceira etapa é
definido um novo modelo Neuro-Fuzzy Hierárquico BSP com
conseqüentes fuzzy (NFHB-Mamdani), cuja implementação
utiliza a arquitetura do NFHBClass para a fase do
aprendizado, teste e validação, porém, com os conseqüentes
diferentes, modificando a estratégia de definição dos
conseqüentes das regras. Além de sua utilização em
classificação de padrões, previsão e controle, o sistema
NFHB-Mamdani é capaz de extrair conhecimento de uma base de
dados em forma de regras do tipo SE ENTÃO. No estudo de
casos são utilizadas duas bases de dados típicas para
aplicações em classificação: Wine e o Iris. Para previsão
são utilizadas séries de cargas elétricas de seis
companhias brasileiras diferentes: Copel, Cemig, Light,
Cerj, Eletropaulo e Furnas. Finalmente, para testar o
desempenho do sistema em controle faz-se uso de uma planta
de terceira ordem como processo a controlar. Os resultados
obtidos para classificação, na maioria dos casos, são
superiores aos melhores resultados encontrados pelos outros
modelos e algoritmos aos quais foram comparados. Para
previsão de cargas elétricas, os resultados obtidos estão
sempre entre os melhores resultados fornecidos por outros
modelos aos quais formam comparados. Quanto à aplicação em
controle, o modelo NFHB-Mamdani consegue controlar, de forma
satisfatória, o processo utilizado para teste.<br>This paper investigates the use of Binary Space
Partitioning (BSP) Hierarchical Neuro-Fuzzy Systems for
applications in pattern classification, forecast, control
systems and obtaining of fuzzy rules. The goal is to create
a BSP Hierarchical Neuro-Fuzzy Model of the Mamdani type
from the BSP Hierarchical Neuro-Fuzzy Class (NFHB-Class)
which is able to create its own structure automatically and
obtain knowledge from a data base through fuzzy rule,
interpreted linguistically, that explain the data structure.
This paper is made up of four main parts: study of the main
Hierarchical Systems; analysis of the BSP Hierarchical
Neuro-Fuzzy Class System, definition and implementation of
the NFHB-Mamdani model, and case studies. A bibliographical
survey is made in the study of the main Hierarchical
Systems. The main Neuro-Fuzzy Models used in control
systems - Falcon and Nefcon -are also investigated.
In the NFHB-Class System, the learning of the structure is
verified, as well as, the recursive partitioning, the
possibility of having a greater number of inputs in
comparison to other Neuro-Fuzzy systems and recursive fuzzy
rules. The NFHB-Class System is a model developed
specifically for pattern classification, since it has
various outputs, it is not possible to use it in control
application and forecast. To make up for this deficiency, a
new unique output model is developed. In the third part, a
new BSP Hierarchical Neuro-Fuzzy model is defined with
fuzzy consequents (NFHB-Mamdani), whose implementation uses
the NFHB-Class architecture for the learning, test, and
validation phase, yet with the different consequents,
modifying the definition strategy of the consequents of the
rules. Aside from its use in pattern classification,
forecast, and control, the NFHB-Mamdani system is capable of
obtaining knowledge from a data base in the form of rules
of the type IF THEN. Two typical data base for application
in classification are used in the case studies: Wine and
Iris. Electric charge series of six different Brazilian
companies are used for forecasting: Copel, Cemig, Light,
Cerj, Eletropaulo and Furnas. Finally, to test the
performance of the system in control, a third order plant
is used as a process to be controlled. The obtained results
for classification, in most cases, are better than the best
results found by other models and algorithms to which they
were compared. For forecast of electric charges, the
obtained results are always among the best supplied by
other models to which they were compared. Concerning its
application in control, the NFHB-Mamdani model is able to
control, reasonably, the process used for test.
2
GarcÃa, Z. Yohn E. "Fuzzy logic in process control: A new fuzzy logic controller and an improved fuzzy-internal model controller." Scholar Commons, 2006. http://scholarcommons.usf.edu/etd/2529.
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Two fuzzy controllers are presented. A fuzzy controller with intermediate variable designed for cascade control purposes is presented as the FCIV controller. An intermediate variable and a new set of fuzzy logic rules are added to a conventional Fuzzy Logic Controller (FLC) to build the Fuzzy Controller with Intermediate Variable (FCIV). The new controller was tested in the control of a nonlinear chemical process, and its performance was compared to several other controllers. The FCIV shows the best control performance regarding stability and robustness. The new controller also has an acceptable performance when noise is added to the sensor signal. An optimization program has been used to determine the optimum tuning parameters for all controllers to control a chemical process. This program allows obtaining the tuning parameters for a minimum IAE (Integral absolute of the error). The second controller presented uses fuzzy logic to improve the performance of the convention
al internal model controller (IMC). This controller is called FAIMCr (Fuzzy Adaptive Internal Model Controller). Twofuzzy modules plus a filter tuning equation are added to the conventional IMC to achieve the objective. The first fuzzy module, the IMCFAM, determines the process parameters changes. The second fuzzy module, the IMCFF, provides stability to the control system, and a tuning equation is developed for the filter time constant based on the process parameters. The results show the FAIMCr providing a robust response and overcoming stability problems. Adding noise to the sensor signal does not affect the performance of the FAIMC.The contributions presented in this work include:The development of a fuzzy controller with intermediate variable for cascade control purposes. An adaptive model controller which uses fuzzy logic to predict the process parameters changes for the IMC controller. An IMC filter tuning equation to update the filter time constant based in the process paramete
rs values. A variable fuzzy filter for the internal model controller (IMC) useful to provide stability to the control system.
3
Karimov, Azar. "Office Rent Variation In Istanbul Cbd: An Application Of Mamdani And Tsk-type Fuzzy Rule Based System." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612322/index.pdf.
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Over the past decade, fuzzy systems have gained remarkable acceptance in many fields including control and automation, pattern recognition, medical diagnosis and forecasting. The fuzzy system application has also been accepted as a promising approach to dealing with uncertainty in real estate valuation analysis. This is mainly due to the necessity of coping with a large number of qualitative and quantitative variables that affect the value of a real property. The appraisers use a great deal of judgment to identify both the characteristics that contribute to property values and the relationships among these characteristics in order to derive estimates of market values. This thesis uses the two widely-used fuzzy rule-based systems<br>namely the Mamdani and Takagi- Sugeno-Kang (TSK) type fuzzy models in an attempt to examine the main determinants of office rents in Istanbul Central Business District (CBD). The input variables of the fuzzy rule-based systems (FRBS) comprise:
i) physical attributes of office spaces and office buildings,
ii) lease contract terms, and
iii) tenants&rsquo<br>perception of the office rent determinants, tenants&rsquo<br>location of residence, tenants&rsquo<br>transportation modes, etc
and as the output the system proposes the office property&rsquo<br>s rental price. Obtaining office rent determinants is a significant issue for both practitioners and academics. While,practitioners use them directly in demand and sensitivity analyses, academics are more interested in the relative significance of these variables and their effect on the variation in office rent to forecast market behavior.
Our data set includes a detailed survey of 500 office spaces located in Istanbul CBD. We have carried out two Mamdani-type FRBS and two TSK-type FRBS for the office space and office building data sets. In these FRBS analyses, firstly the so-called representative office spaces are determined, then the average office space rents are estimated. Finally, the spatial variation in the average office rents across the CBD sub-districts, along with the Office space rent variations with
respect to different clusters, like number of workers, number of floors and so on, have been analyzed. We believe that presenting the spatial variation in office rents will make a noteworthy contribution both to the real estate investors and appraisers interested in Istanbul office market.
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AFONSO, Antônio Cláudio Marques. "Lógica fuzzy aplicada à modelagem da transferência de água em solos." Universidade Federal de Pernambuco, 2009. https://repositorio.ufpe.br/handle/123456789/9849.
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Previous issue date: 2009<br>Conselho Nacional de Desenvolvimento Científico e Tecnológico<br>A modelagem do movimento da água na região não saturada do solo requer
normalmente um grande número de parâmetros e variáveis, tais como a umidade volumétrica
inicial, a umidade volumétrica saturada e a condutividade hidráulica saturada, que podem sem
avaliadas de forma relativamente simples. Outras funções como o potencial matricial e a
condutividade hidráulica relativa, que são funções não lineares da atual umidade volumétrica,
são mais trabalhosas de determinar.
O fluxo monodimensional da água na região não saturada é normalmente modelado
por meio de uma equação diferencial parcial não-linear, conhecida como a equação de
Richards. Desde que essa equação não possa ser resolvida analiticamente em alguns casos
especiais, uma maneira de aproximar sua solução é através de algoritmos numéricos.
O sucesso dos modelos numéricos em descrever a dinâmica da água no solo está
intimamente relacionado com a precisão com que os parâmetros físico-hídricos são
determinados. Este tem sido o grande desafio no uso dos modelos numéricos, pois em geral,
tais parâmetros são difíceis de determinar e apresentam uma grande variabilidade espacial no
solo. Portanto, fazem-se necessários o desenvolvimento e a utilização de metodologias que
incorporem, de uma maneira apropriada, as incertezas intrínsecas ao deslocamento da água
nos solos.
Neste trabalho, modelos fuzzy são usados como uma solução alternativa para
descrever o fluxo de água na zona não saturada do solo. Dois modelos baseados na lógica
fuzzy, desenvolvidos para simular o processo de redistribuição da água no solo, são
apresentados. O princípio desses modelos consiste de um sistema baseado em regras fuzzy do
tipo Mamdani. O conjunto de treinamento foi obtido pela solução numérica da equação de Richards
através do método das diferenças finitas (MDF) e foi utilizado para criar dois modelos
baseados em regras fuzzy. Aqui as regras se baseiam no teor de umidade das camadas
adjacentes do solo.
Dentre as vantagens do modelo fuzzy desenvolvido neste trabalho, estão a sua
simplicidade e o seu baixo custo computacional. O desempenho dos resultados modelados
pelo sistema fuzzy são avaliados através da evolução dos perfis de umidade ao longo do
tempo comparados com os obtidos através da simulação numérica da equação de Richards,
sob duas condições distintas de fronteira inferior e para três solos com diferentes
características hidrodinâmicas.
Os resultados obtidos pelo uso dos modelos fuzzy apresentaram uma satisfatória
reprodução dos valores quando comparados com a solução numérica. Cada um destes
modelos estava devidamente ajustado para cada caso estudado neste trabalho. Este fato
ratificou a possibilidade de que é perfeitamente possível modelar outros casos a partir do uso
da lógica fuzzy, adotando a mesma metodologia proposta e aplicada neste trabalho
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Rodrigues, Diego Garcia. "Um modelo de rede neuro-fuzzy baseada em funções de base radial capaz de inferir regras do tipo Mamdani." reponame:Repositório Institucional da UFSC, 2015. https://repositorio.ufsc.br/xmlui/handle/123456789/132476.
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Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Ciência da Computação, Florianópolis, 2015.<br>Made available in DSpace on 2015-04-29T21:10:19Z (GMT). No. of bitstreams: 1
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Previous issue date: 2015<br>Este trabalho tem como objetivo apresentar um novo sistema de inferência neuro-fuzzy, chamado RBFuzzy, capaz de extrair conhecimento a partir de dados e gerar regras fuzzy do tipo Mamdani com alta interpretabilidade. A RBFuzzy é um sistema de inferência neuro-fuzzy que aproveita o comportamento funcional de neurônios ativados por Funções de Base Radial (RBF) e sua relação com sistemas de inferência fuzzy. A arquitetura da rede RBFuzzy permite extrair um conjunto de regras linguísticas a partir da estrutura conexionista e dos pesos ajustados de uma rede neural. Uma extensão do algoritmo da otimização da colônia de formigas (ACO, do inglês ant colony optimization algorithm) é utilizada para ajustar os pesos de cada regra para gerar um conjunto de regras fuzzy acurado e interpretável. Tendo um conjunto de regras fuzzy um especialista pode adicionar regras novas para incorporar conhecimento novo ao modelo de previsão gerado e também corrigir regras que foram geradas por dados imprecisos.<br><br>Abstract : This work presents a novel neuro-fuzzy inference system, called RBFuzzy, capable of knowledge extraction and generation of highly interpretable Mamdani-type fuzzy rules. RBFuzzy is a four layer neuro-fuzzy inference system that takes advantage of the functional behavior of Radial Basis Function (RBF) neurons and their relationship with fuzzy inference systems. Inputs are combined in the RBF neurons to compound the antecedents of fuzzy rules. The fuzzy rules consequents are determined by the third layer neurons where each neuron represents a Mamdani-type fuzzy output variable in the form of a linguistic term. The last layer weights each fuzzy rule and generates the crisp output. An extension of the ant-colony optimization (ACO) algorithm is used to adjust the weights of each rule in order to generate an accurate and interpretable fuzzy rule set. For benchmarking purposes some experiments with classic datasets were carried out to compare our proposal with the EFuNN neuro-fuzzy model. The RBFuzzy was also applied in a real world oil well-log database to model and forecast the Rate of Penetration (ROP) of a drill bit for a given oshore well drilling section. The obtained results show that our model can reach the same level of accuracy with fewer rules when compared to the EFuNN, which facilitates understandingthe operation of the system by a human expert.
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Kawamura, Jorge. "Aplicação de um sistema fuzzy para diagnostico de cancer do esofago." [s.n.], 2007. http://repositorio.unicamp.br/jspui/handle/REPOSIP/261873.
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Orientador: Akebo Yamakami<br>Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação<br>Made available in DSpace on 2018-08-09T21:49:58Z (GMT). No. of bitstreams: 1
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Previous issue date: 2007<br>Resumo: Este trabalho tem como objetivo a utilização de métodos de inteligência artificial para diagnosticar câncer do esôfago. Este estudo concentrou-se na utilização dos conceitos de sistemas fuzzy. O emprego de sistemas fuzzy ou sistemas difusos para a área de saúde foi motivado pela deficiência de sistemas inteligentes nesta área e pela simplicidade na sua utilização. O sistema fuzzy apresenta características como a existência de uma região duvidosa (ou região vaga) na análise das informações e seu método de interpretação é mais próximo à linguagem do ser humano. Os modelos de inferência utilizados foram o método de Mamdani e o método Sugeno. São analisadas as vantagens e desvantagens de cada método. A partir das características do câncer do esôfago e dos conceitos de sistemas fuzzy foi desenvolvido um sistema para diagnóstico de câncer do esôfago<br>Abstract: The aim of this work is to use the artificial intelligent methods to diagnose esophagus cancer. The artificial intelligence theme has many areas, so this study concentrated in fuzzy system concepts. The lack of intelligent system in health's area motivated this study and fuzzy theory was chosen by its simplicity. This type of system has characteristics like existence of a doubt region in the information analysis and its interpretation's methods is closer to human language. The inference models used are Mamdani and Sugeno models. The advantages and disadvantages are checked too. From esophagus cancer characteristics and fuzzy system concepts, a system to diagnose esophagus cancer was built<br>Mestrado<br>Telecomunicações e Telemática<br>Mestre em Engenharia Elétrica
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Laghetto, Beatriz Krabbe. "Um modelo matemático para estimar o risco de desenvolver câncer de pulmão por meio de sistemas fuzzy." Universidade Federal de São Carlos, 2016. https://repositorio.ufscar.br/handle/ufscar/8040.
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Previous issue date: 2016-05-19<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)<br>The main aims of this work are to study the ways to model mathematically uncertainties using Fuzzy Sets Theory, and then propose a mathematical model to estimate the risk of an individual developing lung cancer through a system based rule fuzzy. Therefore, we consider risk factors, namely smoking, pollution, history of lung disease, family history and contact with chemical agents such as system input variables fuzzy. Lung cancer is a disease that has no symptoms in its early stages, making the diagnosis more difficult to be done and, therefore, most discovered when the cancer is already advanced. This type of cancer is highly lethal and frequent in the population, an increase of 2 % per year in its worldwide incidence.<br>Os principais objetivos desse trabalho são estudar as formas de modelar matematicamente
certas incertezas por meio da Teoria dos Conjuntos Fuzzy e, em seguida, propor um modelo
matemático para estimar o risco de um indivíduo desenvolver câncer de pulmão por meio
um sistema baseado em regras fuzzy. Para isso, consideramos fatores de risco, tais como,
tabagismo, poluição, histórico de doenças pulmonares, histórico familiar e contato com
agentes químicos como variáveis de entrada do sistema fuzzy. O câncer de pulmão é uma
doença que não apresenta sintomas em suas fases iniciais, tornando o diagnóstico mais
difícil de ser feito e, por isso, a maioria descobre quando o câncer já está avançado. Esse
tipo de câncer é altamente letal e frequente na população, apresentando aumento de 2% ao
ano na sua incidência mundial.
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Zettervall, Hang. "Fuzzy Set Theory Applied to Make Medical Prognoses for Cancer Patients." Doctoral thesis, Blekinge Tekniska Högskola [bth.se], Faculty of Engineering - Department of Mathematics and Natural Sciences, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-00574.
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As we all know the classical set theory has a deep-rooted influence in the traditional mathematics. According to the two-valued logic, an element can belong to a set or cannot. In the former case, the element’s membership degree will be assigned to one, whereas in the latter case it takes the zero value. With other words, a feeling of imprecision or fuzziness in the two-valued logic does not exist. With the rapid development of science and technology, more and more scientists have gradually come to realize the vital importance of the multi-valued logic. Thus, in 1965, Professor Lotfi A. Zadeh from Berkeley University put forward the concept of a fuzzy set. In less than 60 years, people became more and more familiar with fuzzy set theory. The theory of fuzzy sets has been turned to be a favor applied to many fields. The study aims to apply some classical and extensional methods of fuzzy set theory in life expectancy and treatment prognoses for cancer patients. The research is based on real-life problems encountered in clinical works by physicians. From the introductory items of the fuzzy set theory to the medical applications, a collection of detailed analysis of fuzzy set theory and its extensions are presented in the thesis. Concretely speaking, the Mamdani fuzzy control systems and the Sugeno controller have been applied to predict the survival length of gastric cancer patients. In order to keep the gastric cancer patients, already examined, away from the unnecessary suffering from surgical operation, the fuzzy c-means clustering analysis has been adopted to investigate the possibilities for operation contra to nonoperation. Furthermore, the approach of point set approximation has been adopted to estimate the operation possibilities against to nonoperation for an arbitrary gastric cancer patient. In addition, in the domain of multi-expert decision-making, the probabilistic model, the model of 2-tuple linguistic representations and the hesitant fuzzy linguistic term sets (HFLTS) have been utilized to select the most consensual treatment scheme(s) for two separate prostate cancer patients. The obtained results have supplied the physicians with reliable and helpful information. Therefore, the research work can be seen as the mathematical complements to the physicians’ queries.
Book chapters on the topic "Mamdani fuzzy model"
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dela Cerna, Monalee A., and Elmer A. Maravillas. "Mamdani Fuzzy Decision Model for GIS-Based Landslide Hazard Mapping." In Transactions on Engineering Technologies. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-2717-8_5.
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Saraswat, Gaurav, Seetaram Maurya, and Nishchal K. Verma. "Health Monitoring of Main Battle Tank Engine Using Mamdani-Type Fuzzy Model." In Computational Intelligence: Theories, Applications and Future Directions - Volume I. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1132-1_31.
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Majumder, Debasish, Mrinmoy Dam, Rupak Bhattacharjee, Shyam Sundar Santra, Rishiraj Saha, and Soham Saha. "Performance Measurement Model for Wind Power Project Using Mamdani Fuzzy Inference System." In Proceedings of the International Conference on Computing and Communication Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4084-8_24.
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Król, Dariusz, Tadeusz Lasota, Bogdan Trawiński, and Krzysztof Trawiński. "Comparison of Mamdani and TSK Fuzzy Models for Real Estate Appraisal." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74829-8_123.
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Sakharova, Lyudmila V., Sergey V. Rogozhin, and Alexander N. Kuzminov. "Aggregation of Enterprise Bankruptcy Risk Assessments Based on Logit Complex—Mamdani Models and Fuzzy Logic Inference." In Complex Systems: Innovation and Sustainability in the Digital Age. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44703-8_13.
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Burhan Türkşen I. "Development of Fuzzy System Models: Fuzzy Rulebases to Fuzzy Functions." In NATO Science for Peace and Security Series - D: Information and Communication Security. IOS Press, 2010. https://doi.org/10.3233/978-1-60750-611-9-155.
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We first review the development of Fuzzy System Models from &ldquo;Fuzzy Rule bases&rdquo; proposed by Zadeh (1965, 1975) and applied by Mamdani, et al. (1981) to &ldquo;Fuzzy Functions&rdquo; proposed by T&uuml;rksen (2007-2008) and further developed by Celikyilmaz and T&uuml;rksen (2007-2009) in a variety of versions. Next, we also review a complementary development of &ldquo;Fuzzy C-Regression Model&rdquo;, (FCRM) proposed by Hathaway and Bezdek, (1993) as well as a &ldquo;Combined FCM, and FCRM&rdquo; algorithms proposed by H&ouml;ppner and Klawonn (2003).
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Kharola, Ashwani. "Design of a Hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) Controller for Position and Angle Control of Inverted Pendulum (IP) Systems." In Fuzzy Systems. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1908-9.ch014.
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This paper illustrates a comparison study of Fuzzy and ANFIS Controller for Inverted Pendulum systems. IP belongs to a class of highly non-linear, unstable and multi-variable systems which act as a testing bed for many complex systems. Initially, a Matlab-Simulink model of IP system was proposed. Secondly, a Fuzzy logic controller was designed using Mamdani inference system for control of proposed model. The data sets from fuzzy controller was used for development of a Hybrid Sugeno ANFIS controller. The results shows that ANFIS controller provides better results in terms of Performance parameters including Settling time(sec), maximum overshoot(degree) and steady state error.
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Singla, Jimmy. "Intelligent Medical Diagnostic System for Diabetes." In Advances in Social Networking and Online Communities. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-9096-5.ch010.
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In this chapter, the neuro-fuzzy technique has been used for the diagnosis of different types of diabetes. It has been reported in the literature that triangular membership functions have been deployed for Mamdani and Sugeno fuzzy expert systems that have been used for diagnosis of different types of diabetes. The Gaussian membership functions are expected to give better results. In this context, Gaussian membership functions have been attempted in the neuro-fuzzy system for the diagnosis of different types of diabetes in the research work, and improved results have been obtained in terms of different parameters like sensitivity, specificity, accuracy, precision. Further, for the comparative study, the dataset used for neuro-fuzzy expert system developed in this research work has been considered on Mamdani fuzzy expert system as well as Sugeno fuzzy expert system, and it has been confirmed that the result parameters show better values in the proposed model.
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V, Tamilselvi, and T. Ahalya. "FUZZY LOGIC CONTROLLER IN COG AND MAMDANI METHOD." In Futuristic Trends in Contemporary Mathematics & Applications Volume 3 Book 1. Iterative International Publishers, Selfypage Developers Pvt Ltd, 2024. http://dx.doi.org/10.58532/v3bjcm1p1ch11.
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This project uses MATLAB and the MATLAB Fuzzy logic toolbox to describe the fuzzy logic controller process in a water bath temperature control system. The primary goal is to get the water bath system's output temperature as close as possible to the intended set point temperature within the allotted time period while maintaining good performance, stability, smoothness, and the least amount of overshoot. Seven linguistic variables were used in this system, and the fuzzy logic control system used 7*7 matrix rules. In this system, the Gaussian membership function will be used. The inference engines will employ the Max-min (MAMDANI) approach and the center of gravity singleton method for defuzzification. The outcomes of the optimized, effective, and high-performing Simulink model are shown. When the temperature of the desired output is equal to the temperature that the user first defined in the step inputs of the simulation model, the waterbath system produces the desired output temperature.
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Martínez-Barrera, Gonzalo, Osman Gencel, Ahmet Beycioglu, Serkan Subaşı, and Nelly González-Rivas. "Artificial Intelligence Methods and Their Applications in Civil Engineering." In Fuzzy Systems. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1908-9.ch059.
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Simulation of material properties generally involves the development of a mathematical model derived from experimental data. In structural mechanics and construction materials contexts, recent experiments have reported that fuzzy logic (FL), artificial neural networks (ANNs), genetic algorithm (GA), and fuzzy genetic (FG) may offer a promising alternative. They are known as artificial intelligence (AI). In civil engineering, AI methods have been extensively used in the fields of civil engineering applications such as construction management, building materials, hydraulic, optimization, geotechnical and transportation engineering. Many studies have examined the applicability of AI methods to estimate concrete properties. This chapter described the principles of FL methods that can be taught to engineering students through MATLAB graphical user interface carried out in a postgraduate course on Applications of Artificial Intelligence in Engineering, discussed the application of Mamdani type in concrete technology and highlighted key studies related to the usability of FL in concrete technology.
Conference papers on the topic "Mamdani fuzzy model"
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Raharja, Nia Maharani, Iswanto, Oyas Wahyunggoro, and Adha Imam Cahyadi. "Altitude control for quadrotor with mamdani fuzzy model." In 2015 International Conference on Science in Information Technology (ICSITech). IEEE, 2015. http://dx.doi.org/10.1109/icsitech.2015.7407823.
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Akbari, Mohammad Saeid, Ali Akbar Safavi, Navid Vafamand, Tomislav Dragicevic, and Jose Rodriguez. "Fuzzy Mamdani-based Model Predictive Load Frequency Control." In 2020 IEEE 11th International Symposium on Power Electronics for Distributed Generation Systems (PEDG). IEEE, 2020. http://dx.doi.org/10.1109/pedg48541.2020.9244311.
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Alves, Kaike, Rosangela Ballini, and Eduardo Aguiar. "A New Fuzzy Inference System Applied to Time Series Forecasting." In Congresso Brasileiro de Inteligência Computacional. SBIC, 2023. http://dx.doi.org/10.21528/cbic2023-003.
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Fuzzy systems are a class of machine learning introduced by Zadeh that combine accuracy and interpretability. This class of models consists of two main parts, the antecedent and the consequent. While the antecedent is responsible for modeling the inputs, the consequent concerns modeling the output. The literature reports two main types of fuzzy systems: Mamadani and Takagi-Sugeno. While Mamdani uses fuzzy sets in the consequent part, Takagi-Sugeno uses polynomial functions. Consequently, Mamdani provides better understandable models and Takagi-Sugeno more accurate ones. In this paper, we propose a new Takagi-Sugeno model. Still, instead of defining the rules based on the input, the proposed model designs the rules based on the output variation to capture linearities in the output and clusters them in the same rule. The model is applied in the regression problems of benchmark series and real datasets of power transformers. The performance of the proposed model is compared with the performance of classical models and evolving Fuzzy Systems. The results are evaluated using error metrics, the number of final rules, and runtime.
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Yang, Kuihe, and Lingling Zhao. "Load Forecasting Model Based on Amendment of Mamdani Fuzzy System." In 2009 5th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM). IEEE, 2009. http://dx.doi.org/10.1109/wicom.2009.5301638.
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Yang, Kuihe, and Lingling Zhao. "Application of Mamdani Fuzzy System Amendment on Load Forecasting Model." In 2009 Symposium on Photonics and Optoelectronics. IEEE eXpress Conference Publishing, 2009. http://dx.doi.org/10.1109/sopo.2009.5230275.
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Mihálik, Ondrej, and Petr Fiedler. "Means of Obtaining Mamdani Fuzzy Model of Car Driver’s Dynamics." In STUDENT EEICT 2022. Fakulta elektrotechniky a komunikacnich technologii VUT v Brne, 2022. http://dx.doi.org/10.13164/eeict.2022.243.
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Borchyk, Ye Yu. "USE OF FUZZY LOGIC FOR FORECASTING WINTER WHEAT YIELD." In FOOD SECURITY OF UKRAINE IN THE CONDITIONS OF POST-WAR RECOVERY: GLOBAL AND NATIONAL DIMENSIONS. MYKOLAIV NATIONAL AGRARIAN UNIVERSITY, 2025. https://doi.org/10.31521/978-617-7149-86-5-108.
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The article examines the problem of forecasting the yield of winter wheat depending on the value of the main factors determined during the spring stem formation of the grain crop, as well as depending on the timing of sowing the crop. A fuzzy logic forecasting system based on the Mamdani model is developed in the MATLAB application package. The calculation result is compared with the calculation results based on the regression model.
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Riaz, Faisal, Rehana Asif, Hina Sajid, and Muaz A. Niazi. "Augmenting Autonomous Vehicular Communication Using the Appreciation Emotion: A Mamdani Fuzzy Inference System Model." In 2015 13th International Conference on Frontiers of Information Technology (FIT). IEEE, 2015. http://dx.doi.org/10.1109/fit.2015.40.
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Machesa, Mosa, Lagouge K. Tartibu, and Modestus O. Okwu. "Prediction of a Blast Induced Peak Particle Velocity in Mining Operations: A Fuzzy Mamdani and ANFIS-Based Evaluating Methodology." In ASME 2021 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/imece2021-71256.
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Abstract Activities in the mining industries as a result of rock blasting is the cause of extreme rock vibration which is considered a serious environmental hazard. In most cases, explosives are often used for the disintegration of rocks in opencast mine. One of the major challenges often experienced in mining industries is the case of ineffective use of explosive energy while performing such opencast operation, this could lead to disproportionate ground vibration, often measured by peak particle velocity (PPV). To reduce such ground vibration and environmental impediments, it is important to adopt creative models for the effective prediction of PPV. Considering the inevitable impact on rock mass, neighbouring structures and sometimes on human beings, an accurate prediction of ground vibrations and the evaluation of the aftereffects must be carried out prior to the actual blasting event. This research is an exposition of the prediction performance of a blast-induced PPV using a creative model -Fuzzy Mamdani Model (FMM) and a hybrid algorithm -Adaptive Neuro-Fuzzy Inference System (ANFIS), in mining operation. These models are employed to predict the blast-induced PPV, which is a measurement of the movement or vibration of a single earth particle as the shock waves from a particular location or blasting event moves through the system. Experimental dataset used in this research consists of three (3) input variables (change weight per delay, distance and scaled distance) and forty-four (44) record samples; the peak particle velocity represents the experimental result. The dataset is fed into MATLAB 2020 platform as input parameters. Results obtained using the creative and hybrid algorithms were compared based on root mean squared error (RMSE) and correlation coefficient between the experimental and predicted values of the PPV. The regression values obtained are 0.8487 and 0.97729 for the Fuzzy Mamdani model and ANFIS model respectively. From the result obtained, the best vibration prediction was achieved using the ANFIS model. It can be concluded that the ANFIS model gave a better prediction in terms of speed of computation and prediction accuracy. It is recommended that other hybrid algorithms and metaheuristic techniques be introduced and compared with the existing solution models for effective prediction of PPV in mining operations.
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Moreno, Jorge Sa´nchez, Edison Castro Prates de Lima, and Gilberto Bruno Ellwanger. "Prediction on Aging of Reconstitutive Clayey Marine Soils Using Fuzzy-Logic." In ASME 2003 22nd International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2003. http://dx.doi.org/10.1115/omae2003-37088.
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This paper presents an application of the fuzzy logic theory for the prediction of the soil strength depth variation due to aging effects. The results of experimental tests with reconstituted clayey soils of the Bay of Campeche in Mexico were used for the fuzzy rule-based system training. The Evolutionary Strategy (ES) was employed as an optimization method for the learning of the Mamdani-type fuzzy rule base. Fuzzy logic provides an easy and transparent method for incorporating common-sense type reasoning. The fuzzy logic model is based on a decision (inference) process that can be better described on a linguistic level using rules with soft facts. In this manner the clay soil strength through the time could be fuzzily predicted as a function of the mean effective normal stress, the time of consolidation and the water content. Illustrative examples showed that the result of prediction seems to be acceptable in engineering practice.