Supervision of adaptive fuzzy controllers for manufacturing systems

Tamani, Karim; Habchi, Georges; Boukezzoula, Reda

doi:10.1109/cimca.2006.205

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…Under this mechanism, the rules and membership functions are derived or adjusted intuitively by external (human) intelligence or supervision that are based on expertise, experience, understanding, observation, etc., which ultimately improves overall performance and restricts system to specified domain (Tamani et al, 2006).…”

Section: Knowledge Formulation From Conscious Pathmentioning

confidence: 99%

Structure identification and IO space partitioning in a nonlinear fuzzy system for prediction of patient survival after surgery

Khan

Quadri

2017

IJICC

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Purpose As far as the treatment of most complex issues in the design is concerned, approaches based on classical artificial intelligence are inferior compared to the ones based on computational intelligence, particularly this involves dealing with vagueness, multi-objectivity and good amount of possible solutions. In practical applications, computational techniques have given best results and the research in this field is continuously growing. The purpose of this paper is to search for a general and effective intelligent tool for prediction of patient survival after surgery. The present study involves the construction of such intelligent computational models using different configurations, including data partitioning techniques that have been experimentally evaluated by applying them over realistic medical data set for the prediction of survival in pancreatic cancer patients. Design/methodology/approach On the basis of the experiments and research performed over the data belonging to various fields using different intelligent tools, the authors infer that combining or integrating the qualification aspects of fuzzy inference system and quantification aspects of artificial neural network can prove an efficient and better model for prediction. The authors have constructed three soft computing-based adaptive neuro-fuzzy inference system (ANFIS) models with different configurations and data partitioning techniques with an aim to search capable predictive tools that could deal with nonlinear and complex data. After evaluating the models over three shuffles of data (training set, test set and full set), the performances were compared in order to find the best design for prediction of patient survival after surgery. The construction and implementation of models have been performed using MATLAB simulator. Findings On applying the hybrid intelligent neuro-fuzzy models with different configurations, the authors were able to find its advantage in predicting the survival of patients with pancreatic cancer. Experimental results and comparison between the constructed models conclude that ANFIS with Fuzzy C-means (FCM) partitioning model provides better accuracy in predicting the class with lowest mean square error (MSE) value. Apart from MSE value, other evaluation measure values for FCM partitioning prove to be better than the rest of the models. Therefore, the results demonstrate that the model can be applied to other biomedicine and engineering fields dealing with different complex issues related to imprecision and uncertainty. Originality/value The originality of paper includes framework showing two-way flow for fuzzy system construction which is further used by the authors in designing the three simulation models with different configurations, including the partitioning methods for prediction of patient survival after surgery. Several experiments were carried out using different shuffles of data to validate the parameters of the model. The performances of the models were compared using various evaluation measures such as MSE.

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Section: Knowledge Formulation From Conscious Pathmentioning

confidence: 99%

Structure identification and IO space partitioning in a nonlinear fuzzy system for prediction of patient survival after surgery

Khan

Quadri

2017

IJICC

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“…This representation permit to integrate the fuzzy control design in some adaptive or learning strategies to specify the rule base parameters. In our previous work, adaptive fuzzy control strategy has been proposed to adjust some parameters so that the tacking error (surplus) converges to zero [41]. However, the adaptive strategy is time consuming and, in some cases, induces instability problems.…”

Section: Input Flowmentioning

confidence: 99%

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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Supervision of adaptive fuzzy controllers for manufacturing systems

Cited by 2 publications

References 18 publications

Structure identification and IO space partitioning in a nonlinear fuzzy system for prediction of patient survival after surgery

Structure identification and IO space partitioning in a nonlinear fuzzy system for prediction of patient survival after surgery

Intelligent distributed and supervised flow control methodology for production systems

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