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Adaptive Neural Network Fuzzy Inference System for HFC Processes

Year: 2019

Type: article

Abstract: The paper presents the design and implementation of a fuzzy inference system (FIS) trained with adaptive neural networks for the generation of specification references in high frequency current (HFC) hardening processes. The specification references are then further used for the control of the process in obtaining the desired outcomes in terms of material hardening and resistance. The FIS is trained using data obtained from experimentation on an industrial HFC device. The trained FIS is then compared to a manually tuned FIS, resulting from expert and operator designs. The results led to the development of intelligent control interfaces in real time through the ANFIS method

Source: Periodicals of Engineering and Natural Sciences (PEN)

DOI: 10.21533/pen.v7.i1.1497

Authors: Victor Vlădăreanu, Luige Vlădăreanu, Radu Ioan Munteanu, Lucian Capitanu, Emilian M. Dobrescu, Mihai Rădulescu, Vlad Alexandru Grosu

Language: English

Topic: Fuzzy Logic and Control Systems

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funders: European Commission, Academia Româna, Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii, Horizon 2020 Framework Programme, Colegiul Consultativ pentru Cercetare-Dezvoltare şi Inovare

Awards: G2644333275, G2798107379, G3345895242, G3477743816, G3878940427, G4864199281, G4937468798, SMart rObOTs for fire-figHting, G8512683158, G9644609

OpenAlex ID: W7117869259

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