Novel Fuzzy System Identification: Comparative Study and Application for Data Forecasting

Martins, Jefferson Beethoven; Bertone, Ana Maria Amarillo; Yamanaka, Kazushi

doi:10.1109/tla.2019.8986416

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…A new method, which is a combination of a fuzzy clustering procedure and a TSK [43,44] fuzzy inference method, has been developed by Martins et al [29]. The approach has two advantages: gain in accuracy and greater speed of response, in other words, less computational effort.…”

Section: New Fuzzy System Identification Datamentioning

confidence: 99%

A Tour of Type-1 and Interval Type-2 Fuzzy Sets Theory

Jafelice

Bertone

2020

Biological Models via Interval Type-2 Fuzzy Sets

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The uncertainty is the imperfection of knowledge about the natural process or natural state. The statistical uncertainty is the randomness or error that comes from different sources as we use it in a statistical methodology [11]. Castro, Castillo and Martínez (2007) Type-1 Fuzzy SetsStarting this chapter is a succinct review of type-1 fuzzy theory, being Definition 2.1 a description of this fundamental concept. Definition 2.1 A type-1 fuzzy set A is characterized by its membership function μ A defined in the universe of discourse X and μ A (x) ∈ [0, 1] [50]. In other words, a type-1 fuzzy set A can be identified by the graph of its membership function:The values μ A (x) = 1 and μ A (x) = 0 indicate, respectively, the complete membership and non-membership of the element x to A.A classic set B ⊂ X can be interpreted as a type-1 fuzzy set, associating as a membership the characteristic function of B, that is,A unitary set B = {x} along with the characteristic function is a type-1 fuzzy set called a singleton. In addition, in fuzzy language, a classic subset is often called a crisp subset. Definition 2.2For the real number α ∈ (0, 1], the α-level (or α-cut) of type-1 fuzzy set A is defined by the set

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Section: New Fuzzy System Identification Datamentioning

confidence: 99%

A Tour of Type-1 and Interval Type-2 Fuzzy Sets Theory

Jafelice

Bertone

2020

Biological Models via Interval Type-2 Fuzzy Sets

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“…The proposed method in this work is based on a fuzzy identification technique whose process has two components: fuzzy clustering and a TS fuzzy inference [8]. In fact, a data set collected is organized in inputs and output for the first stage of the method, consisting in a clustering of the data set by a fuzzy similarity.…”

Section: Introductionmentioning

confidence: 99%

Universal Approximators: New Approach for the Curve Fit of the COVID-19 Infected Population

Bertone

Martins

Jafelice

2022

TCAM

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Fuzzy systems that include Takagi-Sugeno inference method with linear outputs, are widely known to have the ability to uniformly approximate any polynomial with high precision and, as a consequence, any continuous function, by applying the approximation theorem of Weierstrass. There is one more advantage for these methods which is to obtain an explicit expression of the defuzzified output as a function of the system’s inputs. The purpose of this study is to describe the dynamics of a data set collected through the behavior of the tangential envelope and the local concavity of a curve to be adjusted. The functions that define the envelope and its concavity are identified by means of a hybrid system that combines a fuzzy clustering with the qualities of the Takagi-Sugeno inference method. The analyzed data set represents the world population of confirmed infected people by the infectious disease caused by the coronavirus of severe acute respiratory syndrome, named COVID-19. The proposed fuzzy method, in two versions, first and second order, are compared with curves built through the least square method with the maximum of the absolute value of the difference between the fit values and the data, normalized at each instant. In these comparisons, both fuzzy approaches proposed in this study are the ones that best match the data collected, being the fuzzy approximation of second order the best of all.

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A Fuzzy Identification Method Based on the Likelihood Function and Noise Clustering Algorithm

Tsai

Chen

2022

Int. J. Fuzzy Syst.

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Novel Fuzzy System Identification: Comparative Study and Application for Data Forecasting

Cited by 3 publications

References 21 publications

A Tour of Type-1 and Interval Type-2 Fuzzy Sets Theory

A Tour of Type-1 and Interval Type-2 Fuzzy Sets Theory

Universal Approximators: New Approach for the Curve Fit of the COVID-19 Infected Population

A Fuzzy Identification Method Based on the Likelihood Function and Noise Clustering Algorithm

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