scholarly journals Parameter Tuning of Fuzzy Inference Method Using Training Data Composed of Fuzzy Sets

1996 ◽  
Vol 8 (2) ◽  
pp. 247-260
Author(s):  
Takuya OYAMA ◽  
Shun'ichi TANO
Keyword(s):  
Fuzzy Sets ◽  
Fuzzy Inference ◽  
Parameter Tuning ◽  
Training Data ◽  
Inference Method

Fuzzy Inference with Schemes for Guaranteeing Convexity and Symmetricity in Consequences Based on α-Cuts

2009 ◽  
Vol 13 (2) ◽  
pp. 135-149 ◽  
Author(s):  
Kiyohiko Uehara ◽  
◽  
Takumi Koyama ◽  
Kaoru Hirota ◽  
Keyword(s):  
Fuzzy Sets ◽  
Fuzzy Inference ◽  
Fuzzy Rules ◽  
Simulation Studies ◽  
Inference Method ◽  
Control Scheme

A fuzzy inference method is proposed on the basis of α-cuts, which can mathematically prove to deduce consequences in both convex and symmetric forms under the required conditions, studied here, when fuzzy sets in the consequent parts of fuzzy rules are all convex and symmetric. The inference method can reflect the distribution forms of fuzzy sets in consequent parts of fuzzy rules, guaranteeing the convexity in deduced consequences. It also has a control scheme for the fuzziness and specificity in deduced consequences. The controllability provides a way to suppress excessive fuzziness increase and specificity decrease in deduced consequences. Simulation studies show that the proposed method can deduce consequences in convex and symmetric forms under the required conditions. It is also demonstrated that the distribution forms of consequent parts are reflected to deduced consequences. Moreover, it is found that the fuzziness and specificity of deduced consequences can be effectively controlled in the simulations.

An Extended Method of SIRMs Connected Fuzzy Inference Method Using Kernel Method

2009 ◽  
Vol E92-A (10) ◽  
pp. 2514-2521
Author(s):  
Hirosato SEKI ◽  
Fuhito MIZUGUCHI ◽  
Satoshi WATANABE ◽  
Hiroaki ISHII ◽  
Masaharu MIZUMOTO
Keyword(s):  
Fuzzy Inference ◽  
Kernel Method ◽  
Inference Method

scholarly journals Evaluating Power Rehabilitation Actions Using a Fuzzy Inference Method

2021 ◽  
Author(s):  
Yo-Ping Huang ◽  
Wen-Lin Kuo ◽  
Haobijam Basanta ◽  
Si-Huei Lee
Keyword(s):  
Fuzzy Inference ◽  
Inference Method

Parallel Fuzzy Inference Method for Higher Order Takagi–Sugeno Systems

2018 ◽  
Vol 54 (6) ◽  
pp. 1003-1012
Author(s):  
S. V. Yershov ◽  
R. M. Ponomarenko
Keyword(s):  
Fuzzy Inference ◽  
Higher Order ◽  
Inference Method ◽  
Takagi Sugeno

scholarly journals A weighted fuzzy inference method

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Jifang Wang ◽  
Donghua Gu ◽  
QingE Wu ◽  
Yuhao Du
Keyword(s):  
Fuzzy Inference ◽  
Inference Method

scholarly journals Design of an Efficient Maximum Power Point Tracker Based on ANFIS Using an Experimental Photovoltaic System Data

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 858 ◽  
Author(s):  
Sadeq D. Al-Majidi ◽  
Maysam F. Abbod ◽  
Hamed S. Al-Raweshidy
Keyword(s):  
Fuzzy Inference ◽  
Experimental Tests ◽  
Climatic Conditions ◽  
Maximum Power ◽  
Training Data ◽  
Photovoltaic System ◽  
Maximum Power Point ◽  
Inference System ◽  
Photovoltaic Array ◽  
Power Point

Maximum power point tracking (MPPT) techniques are a fundamental part in photovoltaic system design for increasing the generated output power of a photovoltaic array. Whilst varying techniques have been proposed, the adaptive neural-fuzzy inference system (ANFIS) is the most powerful method for an MPPT because of its fast response and less oscillation. However, accurate training data are a big challenge for designing an efficient ANFIS-MPPT. In this paper, an ANFIS-MPPT method based on a large experimental training data is designed to avoid the system from experiencing a high training error. Those data are collected throughout the whole of 2018 from experimental tests of a photovoltaic array installed at Brunel University, London, United Kingdom. Normally, data from experimental tests include errors and therefore are analyzed using a curve fitting technique to optimize the tuning of ANFIS model. To evaluate the performance, the proposed ANFIS-MPPT method is simulated using a MATLAB/Simulink model for a photovoltaic system. A real measurement test of a semi-cloudy day is used to calculate the average efficiency of the proposed method under varying climatic conditions. The results reveal that the proposed method accurately tracks the optimized maximum power point whilst achieving efficiencies of more than 99.3%.

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