An improved design procedure for fuzzy control systems

Kouatli, Issam; Jones, Ben

doi:10.1016/0890-6955(91)90055-8

Cited by 37 publications

(15 citation statements)

References 1 publication

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“…(2)- (16). It must be considered in this case that the new variables are obtained changing P H by C NO and f 1 , g 1 and h 1 , by f 2 , g 2 and h 2 .…”

Section: Obtaining the Process Modelmentioning

confidence: 97%

“…where Â f1 , Â g1 y Â h1 represent the parameters that deÿne the functions of property of the antecedent part, and the consequent constants TS of the rules deÿned by (3), (10) and (11), and Á is the learning-rate parameter that can be di erent in (14)- (16). Fig.…”

Section: Obtaining the Process Modelmentioning

confidence: 99%

“…Fuzzy control seems to be one of the most important areas of application of fuzzy logic [1,5,8,[14][15][16]18,24,25,30].…”

Section: If (X Is A) Then (Y Is B)mentioning

confidence: 99%

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Multivariable fuzzy control applied to the physical–chemical treatment facility of a Cellulose factory

Andújar

Bravo

2005

Fuzzy Sets and Systems

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“…(2)- (16). It must be considered in this case that the new variables are obtained changing P H by C NO and f 1 , g 1 and h 1 , by f 2 , g 2 and h 2 .…”

Section: Obtaining the Process Modelmentioning

confidence: 97%

Section: Obtaining the Process Modelmentioning

confidence: 99%

See 1 more Smart Citation

Multivariable fuzzy control applied to the physical–chemical treatment facility of a Cellulose factory

Andújar

Bravo

2005

Fuzzy Sets and Systems

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“…2 fuzzy set for pain intensity, there exist six empty spaces in Table 1, where it is impossible to have rules. There are many shapes (DOMBI, 1990;KOUATLI and JONES, 1991) of possible membership functions, such as triangle, trapezoid etc., that can be used in the fuzzy logic controller. For simplicity and popularity, a triangle shape is used, and a 25% overlap for contiguous fuzzy sets is reckoned (KOSKO, 1991), as shown in Fig.…”

Section: Control System Designmentioning

confidence: 99%

Pain model and fuzzy logic patient-controlled analgesia in shock-wave lithotripsy

Shieh

Chang

Wang

et al. 2002

Med. Biol. Eng. Comput.

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Pain control in conscious patients was investigated using a push-button, demand-driven supply of drugs. A fuzzy logic patient-controlled analgesia (PCA) algorithm was compared with a conventional algorithm, for alfentanil administration in extracorporeal shock-wave lithotripsy. The conventional PCA algorithm used an initial dose of 0.25mg, a fixed infusion rate of 60 mg h(-1) and a fixed bolus size of 0.2 mg with a 1 min lockout. The fuzzy logic PCA algorithm used an initial dose of 0.25 mg, a changeable infusion rate and a bolus size of 0.1 or 0.05 mg. The infusion rate was adjusted according to a look-up table that accepted the button-pressing history over the last three lockout intervals. The look-up table was designed using fuzzy logic. The bolus size was adjusted according to the button-pressing history over the past two lockout intervals. Twelve patients were treated using conventional PCA, and thirteen were treated with PCA + fuzzy logic control (FLC). PCA + FLC patients consumed 45% less drug. Also, PCA + FLC patients had a mean delivery/demand ratio of 82%, compared with 60% in conventional PCA. When the pain intensity scale was analysed, PCA + FLC patients had acceptable pain intensity at 62%, compared with 44% in conventional PCA.

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“…Though earlier applications were mainly in the area of sociology, medicine, psychology and management, recent successful studies on various engineering problems, such as fuzzy plasticity [7], roughness prediction [8], ball bearing wear [9,10], tool wear recognition [11,12], design techniques [13], fuzzy advisory systems for grinding [14][15][16][17] and general applications in complex systems [18], have demonstrated the feasibility of this approach to study the complex wear systems with MMCs.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of critical wear transition loads of MMCs by rule based fuzzy modelling

Zhang

Mai

1996

Tribol Lett

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The evaluation of critical transition loads in a wear system of metal matrix composites (MMCs) was attempted with the aid of the wear knowledge established using fuzzy rule based modelling. The model presumes that the membership function of the inputs and the rule base are proper. The membership function of the output is adjusted to minimise the error between the predicted output and the experimental results. The influencing parameters considered in this paper include particle size, composite hardness and particle volume fraction, The method shows its unique advantage to model the wear of the complex system with metal matrix composites.

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An improved design procedure for fuzzy control systems

Cited by 37 publications

References 1 publication

Multivariable fuzzy control applied to the physical–chemical treatment facility of a Cellulose factory

Multivariable fuzzy control applied to the physical–chemical treatment facility of a Cellulose factory

Pain model and fuzzy logic patient-controlled analgesia in shock-wave lithotripsy

Evaluation of critical wear transition loads of MMCs by rule based fuzzy modelling

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