<i>MIN</i> and <i>MAX</i> Operators for Trapezoidal Fuzzy Intervals PART I: Formalization and Application

Megri, Fayçal; Boukezzoula, Reda

doi:10.1109/fuzzy.2008.4630694

Cited by 1 publication

(2 citation statements)

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“…In this case, the arithmetic operations used and defined for conventional intervals can be directly extended to the fuzzy ones according to the profiles representation [9,21]. In the same way, the MIN and MAX operations for fuzzy intervals can be easily obtained [10,11,28,29]. In order to illustrate the aggregation of the objectives, the Choquet integral is used [18,19,30].…”

Section: The Aggregation Methodology Of Objectivesmentioning

confidence: 99%

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Intelligent distributed and supervised flow control methodology for production systems

Tamani

Boukezzoula

Habchi

2009

Engineering Applications of Artificial Intelligence

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This paper deals with the development of an intelligent distributed and supervised control approach for highvolume production systems in which the flow of parts can be approximated by a continuous (fluid) model. The proposed approach is based on the decomposition of the production system into elementary modules in order to reduce the control design computational complexity. In this context, a two levels control structure is proposed. At the local level, a surplus-based principle is adopted to regulate the production flow for each module according to the distributed structure. The proposed control methodology decides how to adjust the production rate in order to avoid system overloading and eliminate machine starvation or blocking. In this context, the local control law is synthesized by using the Takagi-Sugeno fuzzy systems. At the high level, a supervisory controller is designed to improve the overall system performances. A supervisor provides an additive component for each local controller when the overall system performances deviate from their acceptable domains (degraded mode). This is done by combining both local and global information into a unified formalism by using aggregation operators and according to fuzzy interval representation of the desired objectives. Finally, the feasibility of the proposed methodology is validated with simulation examples.

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Section: The Aggregation Methodology Of Objectivesmentioning

confidence: 99%

“…Referring to the values in Table 5 and Table 6, equation (28) leads to: 5  WIP 1 (t)  31 and 3  WIP 2 (t)  12. Thus, the related tolerance intervals are chosen as follows: P 12 obj = [5,10]; P 22 obj = [3,6].…”

Section: Semiconductor Applicationmentioning

confidence: 99%