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

Efficiency of Equipment State Assessment System Implementation

2014 ◽  
Vol 698 ◽  
pp. 690-693
Author(s):  
Alexandra I. Khalyasmaa ◽  
Denis A. Baltin ◽  
Natalia A. Babushkina
Keyword(s):  
Performance Analysis ◽  
Power Plants ◽  
Fuzzy Inference ◽  
State Assessment ◽  
Assessment System ◽  
Automated System ◽  
Actual State ◽  
Inference Method ◽  
Equipment State ◽  
Takagi Sugeno

The article is concerned with problems of modern power industry in Russia in the context of technical state assessment of power plants and substations' equipment. Operations algorithm of automated model implemented with the use of Takagi-Sugeno fuzzy inference method are presented in the article. Performance analysis of implementation of automated system of equipment actual state assessment in toolset of power plants maintenance services was performed.

scholarly journals FUZZY INFERENCE SYSTEMS BASE ON POLYNOMIALCONSEQUENTS OF FUZZY RULES

2020 ◽  
Author(s):  
R. Ponomarenko ◽  
A. Dyka
Keyword(s):  
Fuzzy Systems ◽  
Intelligent Systems ◽  
Fuzzy Inference ◽  
Higher Order ◽  
Fuzzy Rules ◽  
Fuzzy Inference Systems ◽  
Logical Conclusion ◽  
Input Variables ◽  
Inference Systems ◽  
Takagi Sugeno

Various fuzzy inference systems that operate on the basis of polynomial consequents of fuzzy rules. As well as inference methods for such systems, in particular, Takagi-Sugeno fuzzy inference systems, their differences from other popular fuzzy systems, such as Mamdani systems, etc., are considered. The attention is focused on the features of the functioning of such systems both in the construction of elementary fuzzy systems. The Systems for which the calculation of the general logical conclusion involves intermediate levels of logical inference with many hierarchically interconnected blocks of fuzzy rules. Fuzzy sets of type 2 are considered, the membership index of which is a fuzzy term of the first type. This allows you to take into account the secondary fuzziness of linguistic concepts in the design of intelligent systems based on fuzzy inference. Fuzzy systems of the second type based on Takagi-Sugeno systems and the iterative Karnik-Mendel algorithm are considered to obtain a logical conclusion for fuzzy systems with the interval membership functions of the second type in the antecedents of fuzzy rules. The proposed procedure for lowering the order of fuzzy rules for higher-order Takagi-Sugeno fuzzy systems is described and justified. A fuzzy inference method for higher-order fuzzy systems based on the partition of a set of input variables is proposed. It is proposed to build a separate block of fuzzy rules for each of the input subspaces in the presence of a common polynomial. Which is a higher-order consequent, that reduces the total number of fuzzy rules in blocks.

Methods of parallel computing for multilevel fuzzy Takagi – Sugeno systems

2016 ◽  
pp. 141-149
Author(s):  
S.V. Yershov ◽  
◽  
R.М. Ponomarenko ◽  
Keyword(s):  
Dynamic Models ◽  
Fuzzy Inference ◽  
Knowledge Bases ◽  
Comparative Characteristic ◽  
Software Systems ◽  
Scientific Papers ◽  
Speed Up ◽  
Diagnostic Software ◽  
Takagi Sugeno

Parallel tiered and dynamic models of the fuzzy inference in expert-diagnostic software systems are considered, which knowledge bases are based on fuzzy rules. Tiered parallel and dynamic fuzzy inference procedures are developed that allow speed up of computations in the software system for evaluating the quality of scientific papers. Evaluations of the effectiveness of parallel tiered and dynamic schemes of computations are constructed with complex dependency graph between blocks of fuzzy Takagi – Sugeno rules. Comparative characteristic of the efficacy of parallel-stacked and dynamic models is carried out.

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

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

Fuzzy Rule Interpolation

2011 ◽  
pp. 728-733 ◽  
Author(s):  
Szilveszter Kovács
Keyword(s):  
Fuzzy Inference ◽  
Fuzzy Rule ◽  
Fuzzy Reasoning ◽  
Fuzzy Relation ◽  
Rule Base ◽  
Rule Based ◽  
Fuzzy Rule Base ◽  
Current Observation ◽  
Rule Bases ◽  
Takagi Sugeno

The “fuzzy dot” (or fuzzy relation) representation of fuzzy rules in fuzzy rule based systems, in case of classical fuzzy reasoning methods (e.g. the Zadeh-Mamdani- Larsen Compositional Rule of Inference (CRI) (Zadeh, 1973) (Mamdani, 1975) (Larsen, 1980) or the Takagi - Sugeno fuzzy inference (Sugeno, 1985) (Takagi & Sugeno, 1985)), are assuming the completeness of the fuzzy rule base. If there are some rules missing i.e. the rule base is “sparse”, observations may exist which hit no rule in the rule base and therefore no conclusion can be obtained. One way of handling the “fuzzy dot” knowledge representation in case of sparse fuzzy rule bases is the application of the Fuzzy Rule Interpolation (FRI) methods, where the derivable rules are deliberately missing. Since FRI methods can provide reasonable (interpolated) conclusions even if none of the existing rules fires under the current observation. From the beginning of 1990s numerous FRI methods have been proposed. The main goal of this article is to give a brief but comprehensive introduction to the existing FRI methods.

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