An Interval Type-2 Fuzzy Logic Toolbox for Control Applications

Castro, Juan R.; Castillo, Oscar; Melín, Patricia

doi:10.1109/fuzzy.2007.4295341

Cited by 130 publications

(81 citation statements)

References 17 publications

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“…Os programas utilizados para obter estes valores foram fornecidos por [2] e [9]. Depois de vários testes realizados no toolbox "sftool" do Matlab, encontramos a função (5) de duas variáveis através do método dos mínimos quadrados e observando que o valor do coeficiente de determinação (r 2 ) deve estar próximo de 1.…”

Section: Comparações Entre As Superfíciesunclassified

Modelagem da dinâmica de um grupo de indivíduos HIV positivos com parâmetro Fuzzy do tipo 2

Castillo¹,

Jafelice

Santana

2015

Proceeding Series of the Brazilian Society of Computational and Applied Mathematics

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Resumo: Neste trabalho estudamos a taxa de retorno de um grupo de indivíduos HIV positivos, de sintomáticos (presença de doenças oportunistas) para assintomáticos, utilizando tratamento antirretroviral. Construímos Sistemas Baseados em IntroduçãoA lógica fuzzy do tipo 1, também conhecida como lógica nebulosa ou lógica difusa,é capaz de trabalhar com a incerteza relacionada ao significado das palavras utilizando funções de pertinências precisas. Uma vez que as funções de pertinências do tipo 1 são definidas, toda incerteza relacionada com o significado das palavras desaparece, porque as funções de pertinência do tipo 1 são totalmente precisas [4] e [6]. A lógica fuzzy do tipo 2, por outro lado, modela a incerteza oriunda do significado das palavras. Embora a função de pertinência do tipo 2 também seja precisa, estaé composta por uma "mancha" de incerteza que permite que a incerteza seja trabalhada pelo SBRF do tipo 2 [4] e [6]. A lógica fuzzy do tipo 1 e do tipo 2 foram introduzidas por Lofti Zadeh em 1965 [8] e 1975 [10], respectivamente. Mendel et al. (2006 afirmam que SBRF do tipo 2 tem potencial para fornecer melhor desempenho do que um SBRF tipo 1. A lógica fuzzy pode ser uma ferramentaútil na abordagem de problemas em biomedicina, a aplicação dessa teoria naárea médica tem demonstrado a sua capacidade para aprimorar e desenvolver tanto equipamentos quanto modelos nas mais diversas atividades de pesquisa e de diagnósticos.Neste trabalho, utilizamos exames laboratoriais de carga viral e do linfócito T, do tipo CD4+ de dez indivíduos HIV positivos. Estes exames laboratoriais foram realizados em datas e em quantidade diferentes. Existe dificuldade de encontrar pessoas com disciplina para participar

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Section: Comparações Entre As Superfíciesunclassified

Modelagem da dinâmica de um grupo de indivíduos HIV positivos com parâmetro Fuzzy do tipo 2

Castillo¹,

Jafelice

Santana

2015

Proceeding Series of the Brazilian Society of Computational and Applied Mathematics

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“…Hence, to enhance the real-time performance of on-line ap-plications, the interval type-2 fuzzy logic was investigated in various articles (e.g., Castro, Castillo and Martinez [10,11] ). So, Simplified interval fuzzy sets have been used in most of T2FLC's used for on-line control.…”

Section: Introductionmentioning

confidence: 99%

“…[9] But the interval FLC had less flexibility than T2FLC, and cannot minimize the uncertainty efficiently because the secondary grade of MF is not considered in the interval FLC. Totally, paper's innovations are expressed in three parts: first, we present a new expression of the triangular type-2 membership function, which results in minimizing the effect of the uncertainties to apply the one's opinions; second, we will divide the problem into some sub-problems to calculate the union of all qualified consequent T2MFs-that will be mentioned in section VI and will be showed in Figure 4; third, to design the T2FLC-not Interval-we will merge the last two steps of controller architecture, Type Reducer and Defuzzifier, into one step with formula (10). All these innovations lead to reduction of computational load and increasing the performance of our T2FLC so that it can be used on line in dynamic systems.…”

Section: Introductionmentioning

confidence: 99%

On-line control of nonlinear systems using a novel type-2 fuzzy logic method

Safarinejadian¹,

Karimi²

2014

AIR

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In this paper, a new type-2 fuzzy logic controller (T2FLC) is proposed for on-line control of nonlinear systems. The proposed method provides more flexibility in applying human thinking in control system design in comparison with the traditional or even type-1 fuzzy logic controllers. One encounters some problems using the type-2 fuzzy logic controllers on-line because of the large volume of computations required. The main aim of this paper is to propose a new type-2 fuzzy controller with a small computational burden so that it can be used on-line. In the proposed T2FLC, the programs related to the computation of union of all qualified consequent T2MFs are divided into some sub-problems, and to design the T2FLC -not interval -we merge the Type Reducer and Defuzzifier into one step. By doing so, the computational time reduces, and the performance of our T2FLC increases so that it can be used on-line in dynamic systems. We study the noise concealing properties of the proposed T2FLC as well. The proposed method is applied to two non-linear dynamic systems, ball & beam and inverted pendulum. The results are compared with PID and type-1 fuzzy logic controllers. In the simulation results, it is also shown that the proposed controller has good noise cancelling properties.

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“…A fuzzy logic controller (FLC) is widely used today to control any plant; the main advantage of using it is that it does not require knowledge of the mathematical model, and in the following papers type 1 fuzzy controllers have been successfully used [41,42]. In Figure 1 the generic representation of the FLC is illustrated.…”

Section: Fuzzy Controllersmentioning

confidence: 99%

Fuzzy Dynamic Parameter Adaptation in the Harmony Search Algorithm for the Optimization of the Ball and Beam Controller

Peraza

Valdez

Castro

et al. 2018

Advances in Operations Research

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This paper presents a method for dynamic parameter adaptation in the harmony search algorithm (HS) based on fuzzy logic. The adaptation is performed using Type 1 (FHS), interval Type 2 (IT2FHS), and generalized Type 2 (GT2FHS) fuzzy systems as the number of improvisations or iterations advances, achieving a better intensification and diversification. The main contribution of this work is the dynamic parameter adaptation using different types of fuzzy systems in the harmony search algorithm applied to optimization of the membership functions for a benchmark control problem; in this case it is focused on the ball and beam controller. Experiments are presented with the HS, FHS, IT2FHS, and GT2FHS with noise (uniform random number) and without noise for the controller, and the following error metrics are obtained: ITAE, ITSE, IAE, ISE, and RMSE, to validate the efficacy of the proposed methods.

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An Interval Type-2 Fuzzy Logic Toolbox for Control Applications

Cited by 130 publications

References 17 publications

Modelagem da dinâmica de um grupo de indivíduos HIV positivos com parâmetro Fuzzy do tipo 2

Modelagem da dinâmica de um grupo de indivíduos HIV positivos com parâmetro Fuzzy do tipo 2

On-line control of nonlinear systems using a novel type-2 fuzzy logic method

Fuzzy Dynamic Parameter Adaptation in the Harmony Search Algorithm for the Optimization of the Ball and Beam Controller

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