T-S Fuzzy Model as Universal Approximator

2003 ◽  
pp. 277-289
Keyword(s):  
Fuzzy Model ◽  
Universal Approximator

Inverse Fuzzy Model Controllers

1993 ◽  
Author(s):  
Celal Batur ◽  
Arvind Srinivasan ◽  
Chien-Chung Chan
Keyword(s):  
Fuzzy Model

NEURO-FUZZY MODEL FOR ENVIRONMENTAL IMPACT ASSESSMENT

2011 ◽  
Vol 10 (3) ◽  
pp. 381-386 ◽  
Author(s):  
Alexandru Trandabat ◽  
Marius Pislaru ◽  
Silvia Avasilcai
Keyword(s):  
Environmental Impact ◽  
Impact Assessment ◽  
Environmental Impact Assessment ◽  
Fuzzy Model ◽  
Neuro Fuzzy

Pengklasifikasian Tingkat Kesejahteraan Keluarga Di Desa Citamiang Dengan Penerapan Logika Fuzzy Model Tahani

2020 ◽  
Vol 5 (2) ◽  
pp. 97-107
Author(s):  
Yoga Permana ◽  
Lelah Lelah
Keyword(s):  
Fuzzy Model

Indonesia merupakan negara yang memiliki populasi penduduk yang cukup besar, pada tahun 2020 jumlah penduduk Indonesia mencapai 269,6 juta jiwa. Setiap dari mereka tentunya memiliki keluarga. Kesejahteraan keluarga tidak hanya berpengaruh terhadap keberhasilan anggota keluarganya, namun juga berpengaruh terhadap keberhasilan pemerintah, tak terkecuali pemerintahan desa. Oleh sebab itu, informasi mengenai tingkat kesejahteraan keluarga diperlukan untuk meninjau upaya yang telah dilakukan pemerintah apakah berhasil ataukah tidak. Untuk menentukan tingkat kesejahteraan keluarga terdapat beberapa indikator seperti penghasilan, pekerjaan, usia dan tanggungan. Supaya proses pengklasifikasian kesejahteraan keluarga bisa lebih efisien maka dapat diolah melalui program yang menerapkan logika fuzzy dengan model Tahani. Tujuan dari penelitian ini dimaksudkan untuk mengklasifikasikan kesejahteraan keluarga berdasarkan data penduduk yang dimiliki oleh pemerintah desa. Berdasarkan hasil penelitian yang diperoleh logika fuzzy dengan model Tahani bisa digunakan untuk mengolah data penduduk yang sesuai dengan indikator tingkat kesejahteraan keluarga dengan memberikan keluaran berupa pengklasifikasian keluarga meliputi keluarga tidak mampu, keluarga prasejahtera dan keluarga sejahtera. Keluaran dari program juga diuji dengan aplikasi fuzzyTECH untuk mengukur keberhasilan penerapan logika fuzzy pada program yang dibangun.

Fuzzy model for grape sugar content and fruit shape index with irrigation quota as parameter

2010 ◽  
Vol 18 (2) ◽  
pp. 348-351
Author(s):  
Tao LI ◽  
Jian-Feng ZHANG ◽  
Jiang-Hui ZHANG ◽  
Quan-Jiu WANG ◽  
Sheng-Jiang ZHANG ◽  
...  
Keyword(s):  
Sugar Content ◽  
Fuzzy Model ◽  
Shape Index ◽  
Fruit Shape

Fuzzy Modeling to Simulate Surface Roughness in CNC Turning of AISI 1045 Steel

2011 ◽  
Vol 486 ◽  
pp. 262-265
Author(s):  
Amit Kohli ◽  
Mudit Sood ◽  
Anhad Singh Chawla
Keyword(s):  
Experimental Data ◽  
Surface Roughness ◽  
Process Parameters ◽  
Fuzzy Model ◽  
Fuzzy Modeling ◽  
Cnc Machine ◽  
Model Based ◽  
Aisi 1045 Steel ◽  
Aisi 1045 ◽  
1045 Steel

The objective of the present work is to simulate surface roughness in Computer Numerical Controlled (CNC) machine by Fuzzy Modeling of AISI 1045 Steel. To develop the fuzzy model; cutting depth, feed rate and speed are taken as input process parameters. The predicted results are compared with reliable set of experimental data for the validation of fuzzy model. Based upon reliable set of experimental data by Response Surface Methodology twenty fuzzy controlled rules using triangular membership function are constructed. By intelligent model based design and control of CNC process parameters, we can enhance the product quality, decrease the product cost and maintain the competitive position of steel.

scholarly journals Neuro-Fuzzy Transformation with Minimize Entropy Principle to Create New Features for Particulate Matter Prediction

2021 ◽  
Vol 11 (14) ◽  
pp. 6590
Author(s):  
Krittakom Srijiranon ◽  
Narissara Eiamkanitchat
Keyword(s):  
Particulate Matter ◽  
Fuzzy Model ◽  
Original Data ◽  
Northern Region ◽  
National Standard ◽  
Minimum Entropy ◽  
Fuzzy Transformation ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Entropy Principle

Air pollution is a major global issue. In Thailand, this issue continues to increase every year, similar to other countries, especially during the dry season in the northern region. In this period, particulate matter with aerodynamic diameters smaller than 10 and 2.5 micrometers, known as PM10 and PM2.5, are important pollutants, most of which exceed the national standard levels, the so-called Thailand air quality index (T-AQI). Therefore, this study created a prediction model to classify T-AQI calculated from both types of PM. The neuro-fuzzy model with a minimum entropy principle model is proposed to transform the original data into new informative features. The processes in this model are able to discover appropriate separation points of the trapezoidal membership function by applying the minimum entropy principle. The membership value of the fuzzy section is then passed to the neural section to create a new data feature, the PM level, for each hour of the day. Finally, as an analytical process to obtain new knowledge, predictive models are created using new data features for better classification results. Various experiments were utilized to find an appropriate structure with high prediction accuracy. The results of the proposed model were favorable for predicting both types of PM up to three hours in advance. The proposed model can help people who are planning short-term outdoor activities.

Characterization of polymer based composite using neuro-fuzzy model

2021 ◽  
Author(s):  
Safaa A.S. Almtori ◽  
Imad O. Bachi Al-Fahad ◽  
Atheed Habeeb Taha Al-temimi ◽  
A.K. Jassim
Keyword(s):  
Fuzzy Model ◽  
Neuro Fuzzy
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