Comparison of intelligent control schemes for real-time pressure control

Babuška, Robert; Braake, H.A.B. te; Can, H.J.L. van; Krijgsman, A.J.; Verbruggen, H.B.

doi:10.1016/0967-0661(96)00174-8

Cited by 48 publications

(13 citation statements)

References 5 publications

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“…The developed fuzzy models implemented based the Takagi-Sugeno technique (Babuska, 1997;Babuska et al, 1996). The proposed technique does not require any a prior knowledge about the operating regimes.…”

Section: Resultsmentioning

confidence: 99%

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Development of Software Reliability Growth Models for Industrial Applications Using Fuzzy Logic

Aljahdali¹

2011

Journal of Computer Science

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Problem statement:The utilization of Software Reliability Growth Models (SRGM) plays a major role in monitoring progress, accurately predicting the number of faults in the software during both development and testing processes; define the release date of a software product, helps in allocating resources and estimating the cost for software maintenance. This leads to achieving the required reliability level of a software product. Approach: We investigated the use of fuzzy logic on building SRGM to estimate the expected software faults during testing process. Results: The proposed fuzzy model consists of a collection of linear sub-models, based on the Takagi-Sugeno technique and attached efficiently using fuzzy membership functions to represent the expected software faults as a function of historical measured faults. A data set provided by John Musa of bell telephone laboratories (i.e., real time control, military and operating system applications) was used to show the potential of using fuzzy logic in solving the software reliability modeling problem. Conclusion: The developed models provided high performance modeling capabilities.

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Section: Resultsmentioning

confidence: 99%

“…The antecedent variables reflect information about the process operating conditions. The consequent of the rule is usually a linear regression model which is valid around the given operating condition (Babuska, 1996;Babuska et al, 1996;Huang et al, 1999;Sheta et al, 2009;Abdelrahman et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Development of Software Reliability Growth Models for Industrial Applications Using Fuzzy Logic

Aljahdali¹

2011

Journal of Computer Science

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“…The modeling methodology described in this article has been successfully applied to many real-world problems in diverse fields, like ecology [16], biotechnology [17], finance [18], and process control [19]. It is our experience that when dealing with practical applications, the transparency of the models is of high importance.…”

Section: Discussionmentioning

confidence: 99%

“…The reader is encouraged to compare the results in this section with those in [3]. Consider a univariate function y(x) = 3e 0x sin( x) + (19) where is Gaussian noise with zero mean and 2 = 0:15. By using random inputs x uniformly distributed in [03; 3], 300 samples of y(x) were obtained from (19) (see Fig.…”

Section: A Ts Fuzzy Model With Linear Consequentsmentioning

confidence: 99%

Rule-based modeling: precision and transparency

Setnes

Babuška

Verbruggen

1998

IEEE Trans. Syst., Man, Cybern. C

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258

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Abstract-This article is a reaction to recent publications on rulebased modeling using fuzzy set theory and fuzzy logic. The interest in fuzzy systems has recently shifted from the seminal ideas about complexity reduction toward data-driven construction of fuzzy systems. Many algorithms have been introduced that aim at numerical approximation of functions by rules, but pay little attention to the interpretability of the resulting rule base. We show that fuzzy rule-based models acquired from measurements can be both accurate and transparent by using a low number of rules. The rules are generated by product-space clustering and describe the system in terms of the characteristic local behavior of the system in regions identified by the clustering algorithm. The fuzzy transition between rules makes it possible to achieve precision along with a good qualitative description in linguistic terms. The latter is useful for expert evaluation, rule-base maintenance, operator training, control systems design, user interfacing, etc. We demonstrate the approach on a modeling problem from a recently published article.

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“…Para alguns processos não-lineares complexos, os requisitos de projeto não podem ser satisfeitos quando méto-dos de controle convencional baseados em modelos lineares do processo são utilizados. Neste contexto, muita atenção deve ser dada ao desenvolvimento de técnicas de identificação e controle não-lineares (Babuška et al, 1996). Adicionalmente, diversas técnicas de controle moderno vêm sendo desenvolvidas nos últimos anos com o objetivo de melhorar o desempenho dos controladores PID (Proporcional, Integral e Derivativo).…”

Section: Introductionunclassified

Controle nebuloso adaptativo por modelo de referência: projeto e aplicação em sistemas não lineares

Callai

Coelho

2007

Sba Controle & Automação

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The main contribution of this paper is the investigation of different model reference adaptation schemes for improving the performance of fuzzy logic control. Adaptive fuzzy control strategies are based on: i) fuzzy control with fuzzy internal model; ii) fuzzy control with fuzzy compensation; and iii) proportional and integral control with fuzzy adaptation. Numerical and experimental results are shown in order to assess the performance of each adaptive fuzzy control algorithm in nonlinear processes.

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Comparison of intelligent control schemes for real-time pressure control

Cited by 48 publications

References 5 publications

Development of Software Reliability Growth Models for Industrial Applications Using Fuzzy Logic

Development of Software Reliability Growth Models for Industrial Applications Using Fuzzy Logic

Rule-based modeling: precision and transparency

Controle nebuloso adaptativo por modelo de referência: projeto e aplicação em sistemas não lineares

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