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.

Investigation of Hierarchical Fuzzy Inference Systems, when Obtaining Integral Estimates of the Analyzed Objects

2021 ◽  
Vol 27 (11) ◽  
pp. 582-591
Author(s):  
A. A. Sorokin ◽  
Keyword(s):  
Fuzzy Inference ◽  
Training Sample ◽  
Strong Increase ◽  
Hierarchical Systems ◽  
Fuzzy Inference Systems ◽  
Machine Learning Methods ◽  
Output Pattern ◽  
Input Variables ◽  
Inference Systems ◽  
Takagi Sugeno

The purpose of this paper is to study the patterns of the formation of output values in hierarchical systems offuzzy inference. Hierarchical fuzzy inference systems (HFIS) are used to aggregate heterogeneous parameters during the assessment of the state of various elements of complex systems. The use of HFIS allows avoiding the "curse" of the dimension associated with a strong increase in the number and complication of the structure of the production rule, which is characteristic of conventional fuzzy inference systems (FIS), which aggregate the results of interaction of different values of input variables in one knowledge base. As part of the research, numerical experiments were carried out to study the features of the formation of output patterns in HFIS, based on FIS using the Mamdani and Takagi-Sugeno algorithms. As a result of the experiment, it was shown that the output values of the studied HFIS tend to be grouped in the region of fixed values, and the output pattern itself acquires a stepwise character. The revealed property allows using HFIS to distribute the objects of the analyzed sample into groups of states. This property can be used to solve problems of distributing objects into groups in conditions when it is difficult to form a training sample for machine learning methods, but at the same time there is knowledge of the expert group about the features of the functioning of the object of research. Additionally, the paper investigates the features of the formation of output patterns depending on the parameters of the membership functions describing the input variables in HFIS, which are based on FIS using the Mamdani algorithm and HFIS, which are based on FIS using the Takagi-Sugeno algorithm.

An approach for non-singleton generalized Type-2 fuzzy classifiers

2020 ◽  
Vol 39 (5) ◽  
pp. 7203-7215
Author(s):  
Emanuel Ontiveros-Robles ◽  
Oscar Castillo ◽  
Patricia Melin
Keyword(s):  
Fuzzy Systems ◽  
General Type ◽  
Fuzzy Inference ◽  
Classification Problems ◽  
Fuzzy Inference Systems ◽  
Classification Rate ◽  
Inference System ◽  
Fuzzy Classifiers ◽  
Inference Systems

In recent years, successful applications of singleton fuzzy inference systems have been made in a plethora of different kinds of problems, for example in the areas of control, digital image processing, time series prediction, fault detection and classification. However, there exists another relatively less explored approach, which is the use of non-singleton fuzzy inference systems. This approach offers an interesting way for handling uncertainty in complex problems by considering inputs with uncertainty, while the conventional Fuzzy Systems have their inputs with crisp values (singleton systems). Non-singleton systems have as inputs Type-1 membership functions, and this difference increases the complexity of the fuzzification, but provides the systems with additional non-linearities and robustness. The main limitations of using a non-singleton fuzzy inference system is that it requires an additional computational overhead and are usually more difficult to apply in some problems. Based on these limitations, we propose in this work an approach for efficiently processing non-singleton fuzzy systems. To verify the advantages of the proposed approach we consider the case of general type-2 fuzzy systems with non-singleton inputs and their application in the classification area. The main contribution of the paper is the implementation of non-singleton General Type-2 Fuzzy Inference Systems for the classification task, aiming at analyzing its potential advantage in classification problems. In the present paper we propose that the use of non-singleton inputs in Type-2 Fuzzy Classifiers can improve the classification rate and based on the realized experiments we can observe that General Type-2 Fuzzy Classifiers, but with non-singleton fuzzification, obtain better results in comparison with respect to their singleton counterparts.

Method for Higher Order polynomial Sugeno Fuzzy Inference Systems

2016 ◽  
Vol 351 ◽  
pp. 76-89 ◽  
Author(s):  
Juan R. Castro ◽  
Oscar Castillo ◽  
Mauricio A. Sanchez ◽  
Olivia Mendoza ◽  
Antonio Rodríguez-Diaz ◽  
...  
Keyword(s):  
Fuzzy Inference ◽  
Higher Order ◽  
Fuzzy Inference Systems ◽  
Order Polynomial ◽  
Inference Systems

scholarly journals A Novel Human STR Similarity Method using Cascade Statistical Fuzzy Rules with Tribal Information Inference

2016 ◽  
Vol 6 (6) ◽  
pp. 3103
Author(s):  
M. Rahmat Widyanto ◽  
Reggio N. Hartono ◽  
Nurtami Soedarsono
Keyword(s):  
Fuzzy Inference ◽  
Promising Result ◽  
Similarity Score ◽  
Fuzzy Rules ◽  
Fuzzy Inference Systems ◽  
Matching Algorithm ◽  
Distribution Probability ◽  
Inference Systems ◽  
Similarity Method ◽  
Short Tandem

<p>A novel human STR (Short Tandem Repeat) similarity method using cascade statistical fuzzy rules with tribal information inference is proposed. The proposed method consists of two cascade Fuzzy Inference Systems (FIS). The first FIS is to discriminate the tribal similarity, and the second FIS is to calculate the STR similarity. By using the allele marker’s statistical distribution probability density function as the membership function in the Fuzzy Rules of the first FIS, the new method makes it possible to tell the tribal similarity between two STR profiles. A 727 data acquired from tribal groups of Indonesia is used to examine the method produced promising result, being able to indicate higher tribal similarity score within a tribal group and lower similarity between tribal groups. In the light of Indonesia’s diverse tribal groups, these properties are able to be leveraged as a new way to improve the versatility of existing DNA matching algorithm.</p>

A Novel Human STR Similarity Method using Cascade Statistical Fuzzy Rules with Tribal Information Inference

2016 ◽  
Vol 6 (6) ◽  
pp. 3103 ◽  
Author(s):  
M. Rahmat Widyanto ◽  
Reggio N. Hartono ◽  
Nurtami Soedarsono
Keyword(s):  
Fuzzy Inference ◽  
Promising Result ◽  
Similarity Score ◽  
Fuzzy Rules ◽  
Fuzzy Inference Systems ◽  
Matching Algorithm ◽  
Distribution Probability ◽  
Inference Systems ◽  
Similarity Method ◽  
Short Tandem

<p>A novel human STR (Short Tandem Repeat) similarity method using cascade statistical fuzzy rules with tribal information inference is proposed. The proposed method consists of two cascade Fuzzy Inference Systems (FIS). The first FIS is to discriminate the tribal similarity, and the second FIS is to calculate the STR similarity. By using the allele marker’s statistical distribution probability density function as the membership function in the Fuzzy Rules of the first FIS, the new method makes it possible to tell the tribal similarity between two STR profiles. A 727 data acquired from tribal groups of Indonesia is used to examine the method produced promising result, being able to indicate higher tribal similarity score within a tribal group and lower similarity between tribal groups. In the light of Indonesia’s diverse tribal groups, these properties are able to be leveraged as a new way to improve the versatility of existing DNA matching algorithm.</p>

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