Integrating expert knowledge into industrial control structures

Galichet, Sylvie; Foulloy, Laurent

doi:10.1016/s0166-3615(03)00129-5

Cited by 8 publications

(3 citation statements)

References 13 publications

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“…This process allows to compare the symbolic knowledge model of experts with the results of the classification system. Galichet and Foulloy (2003) propose several structures to generate collaboration between the numeric and symbolic processing implemented with fuzzy linguistic systems.…”

Section: Fig 7 Fuzzy Sensor Implementation In Vision Systemmentioning

confidence: 99%

An Appearance Fuzzy Sensor Integrating a Knowledge Model

Schmitt

Bombardier

Charpentier

et al. 2006

IFAC Proceedings Volumes

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This article exposes a modeling process of expert's knowledge to improve a system of wooden board appearance classification. The aesthetic criteria for the classification are the color and the texture. The extraction of knowledge concerning these two notions is realized with the Natural language Information Analys Then, to improve the current industrial system, we suggest to use this symbolic is Method (NIAM). knowledge model to generate a numeric model. This numeric part is built thanks to a Fuzzy Rules based Inference System (SIF). Fuzzy Sets Theory is here well adapted in order to obtain not-disjointed result classes and to manipulate subjective or symbolic information. Finally, we propose to create our appearance sensor under the form of a "fuzzy sensor".

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Section: Fig 7 Fuzzy Sensor Implementation In Vision Systemmentioning

confidence: 99%

An Appearance Fuzzy Sensor Integrating a Knowledge Model

Schmitt

Bombardier

Charpentier

et al. 2006

IFAC Proceedings Volumes

View full text Add to dashboard Cite

show abstract

“…Our numeric model relies on a series of fuzzy inferences system which have common structural characteristics. In this perspective of linking symbolic and numeric fields, we noticed an interesting work led by (Galichet and Foulloy, 2003) in which they propose different architectures to "combine conventional regulators and knowledge-based procedures in a unified control structure". In other words, they proposed a framework allowing achieving "a collaboration between numeric and expert processing" implemented with fuzzy linguistic systems.…”

Section: Numeric Modelmentioning

confidence: 99%

A Fuzzy Recognition Model Based on Human Skill Integration

Mazaud

Bombardier

Lhoste³

et al. 2006

IFAC Proceedings Volumes

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This article presents the improvement of a defect recognition system for fibrous products by using knowledge integration from two expert fields. These two kinds of knowledge that we want to integrate respectively concern wood expertise and industrial vision expertise. First, extraction, modelling and integration of knowledge use the Natural language Information Analysis Method (NIAM) to be formalised from their natural language expression. Then, to improve a classical industrial recognition system using vision, we propose to use the resulting symbolic model of knowledge to partially build a numeric model of defect recognition. This model is created according to a tree structure where each inference engine is a Fuzzy Rules based Inference System. The expert knowledge model previously obtained is used to configure each node of the resulting hierarchical structure. The practical results we obtained with industrial data show the efficiency of such an approach.

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“…Several applications of FCSs with Mamdani FCs are reported in manufacturing. They include control of industrial weigh belt feeders (Zhao and Collins, 2003), the realization of specific controllers (Dvorak et al, 2003), (Galichet and Foulloy, 2003), control of machining processes (Haber et al, 2003), (Nandi and Davim, 2009), (Haber et al, 2009), (E. Haber et al, 2010), laser tracking systems (Bai et al, 2005), plastic injection molding (Chen et al, 2008) and vibration suppression (Marinaki et al, 2010). The manufacturing area is related to robotics.…”

Section: Fuzzy Controlmentioning

confidence: 99%

Soft-computing based visual control for unmanned vehicles

Olivares-Méndez¹

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The Cross-Entropy optimization method is not wide used for control in the XII ABSTRACT literature. This Thesis presents the way to optimize the gains, membership function sets' position and size and the rules' weight to improve the behavior of a Fuzzy controller. This optimization process was done using ROS and Matlab Simulink to obtain better results for See and Avoid tests for UAVs. This Thesis demonstrates that the Fuzzy Logic Controllers are widely capable to command free-model systems in high disturbance environments with a low cost sensor. The noisy effects of illumination changes and the high uncertain of the visual detection were manage in a gentle way by this Soft Computing technique to approach different tasks with different aerial and ground vehicles. NomenclatureThe conventions used in this document are defined as follows:Matrix will be represented by capital bold letters such as H or R.Vectors will be represented by a lower case bold letters with or without a upper bar, such as x or t.A vector is a column vector, unless indicated otherwise. Scalar variables are normal italic letters such as i or k.Images are normally represented by capital italic letters such as I or T.

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Integrating expert knowledge into industrial control structures

Cited by 8 publications

References 13 publications

An Appearance Fuzzy Sensor Integrating a Knowledge Model

An Appearance Fuzzy Sensor Integrating a Knowledge Model

A Fuzzy Recognition Model Based on Human Skill Integration

Soft-computing based visual control for unmanned vehicles

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