Fuzzy description of air quality using fuzzy inference system with degree of match via computing with words: a case study

2014 ◽  
Vol 7 (3) ◽  
pp. 325-334 ◽  
Author(s):  
Jyoti Yadav ◽  
Vilas Kharat ◽  
Ashok Deshpande
Keyword(s):  
Air Quality ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Computing With Words ◽  
Inference System ◽  
Fuzzy Description

Sustainable management of water demand using fuzzy inference system: a case study of Kenyir Lake, Malaysia

2021 ◽  
Author(s):  
Nor Najwa Irina Mohd Azlan ◽  
Marlinda Abdul Malek ◽  
Maslina Zolkepli ◽  
Jamilah Mohd Salim ◽  
Ali Najah Ahmed
Keyword(s):  
Fuzzy Inference System ◽  
Water Demand ◽  
Sustainable Management ◽  
Fuzzy Inference ◽  
Inference System ◽  
System A

scholarly journals Competency mapping with Sugeno fuzzy inference system for variable pay determination: A case study

2018 ◽  
Vol 9 (4) ◽  
pp. 2215-2226 ◽  
Author(s):  
Sushri Samita Rout ◽  
Bijan Bihari Misra ◽  
Sasmita Samanta
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Variable Pay

Air quality assessment using a weighted Fuzzy Inference System

2016 ◽  
Vol 33 ◽  
pp. 57-74 ◽  
Author(s):  
Miguel Ángel Olvera-García ◽  
José J. Carbajal-Hernández ◽  
Luis P. Sánchez-Fernández ◽  
Ignacio Hernández-Bautista
Keyword(s):  
Air Quality ◽  
Quality Assessment ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Air Quality Assessment

scholarly journals PEMILIHAN INPUT MODEL ADAPTIVE FUZZY INFERENCE SYSTEM (ANFIS) BERBASIS LAGRANGE MULTIPLIER TEST DILENGKAPI GUI MATLAB (Aplikasi pada Data Harga Beras Kualitas Rendah di Indonesia Periode Januari 2013 – Februari 2019)

2019 ◽  
Vol 8 (4) ◽  
pp. 451-461
Author(s):  
Khusnul Umi Fatimah ◽  
Tarno Tarno ◽  
Abdul Hoyyi
Keyword(s):  
Lagrange Multiplier ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Lagrange Multiplier Test ◽  
Inference System ◽  
Input Selection ◽  
Lm Test ◽  
Rice Prices ◽  
Selection Of

Adaptive Neuro Fuzzy Inference System (ANFIS) is a method that uses artificial neural networks to implement fuzzy inference systems. The optimum ANFIS model is influenced by the selection of inputs, number of membership and rules. In general, the selection of ANFIS input is based on Autoregressive (AR) unit as a result of ARIMA preprocessing. Thus it requires several assumptions. In this research, an alternative selection of ANFIS input based on Lagrange Multiplier Test (LM Test) is used to test hypothesis for the addition of one input. Preprocessing is conducted to obtain the value of partial autocorrelation against Zt. The input lag variable which has the highest partial autocorrelation is the first input ANFIS. The next input selection is selected based on LM test for adding one variable. To test the performance of LM Test, an empirical study of two groups of generated data and low quality rice prices is conducted as a case study. Generating data with stationary and non-stationary criteria has a good performance because it has very good forecasting ability with MAPE out sample for each characteristic are 5.6785% and 9.4547%. In the case study using LM Test, the selected input are and  with the number of membership 2. The chosen model has very good forecasting ability with MAPE outsampel 6.4018%. Keywords : ANFIS, ANFIS Input, LM-Test, Low Quality Rice Prices, Forecasting

Sign in / Sign up

Export Citation Format

Share Document