Theory and simulation of a neuro-fuzzy controller for 20MW turbo generator control

Kebe, Babacar; Thiaw, Lamine; Ba, Oumar

doi:10.1109/icrera.2018.8566897

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Prognosis Of Carbomill Failures By Neuro-fuzzy Approach1

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2019

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“…Several classifications can thus be distinguished: NEFCLASS, NEFDIAG, NEFCON, NEFPROX and ANFIS. The first-class Takagi-Sugeno ANFIS neuro-fuzzy network (outputs of subsystems are linear combinations of inputs) [25], [26] [27] and [28] is an effective tool for approximating non-linear functions. It has the advantage of having an adaptive structure to all the input data of the network.…”

Section: Prognosis Of Carbomill Failures By Neuro-fuzzy Approachmentioning

confidence: 99%

Graphical Interfaces for Dynamic Supervision for Failures Prognosis using the AI-PLC Combinatorial Approach: The Case Study of Cameroon Breweries Mill

Kombe

Nzeneu

2019

IJASRE

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The increasing complexity of industrial systems and the increasingly severe operating constraints have forced specialists to design, specify and operate modern industrial processes. These evolutions thus imply the development of "intelligent" supervisory and prognostic systems, for the improvement of the control of the processes and the realization of the maintenance actions. This article presents our contribution in the study and the development of an interface of supervision and failures prediction of the Carbomill (malt mill) of the Breweries of Cameroon. The methodological approach is based on the design of a graphical interface made under Vijeo and controlled by an PLC, programmed on Unity Pro XL and the use of an ANFIS neuro-fuzzy network as a prediction tool. The expected results lead at the end of the learning on the evaluation by an RMSE cost function of 0.2142.

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Section: Prognosis Of Carbomill Failures By Neuro-fuzzy Approachmentioning

confidence: 99%