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Soft Computing

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30.01.2017 | Methodologies and Application

Input value prediction of parameters in laser bending using Fuzzy and PSO

verfasst von: M. R. Nejati, M. Hoseinpour Gollo, M. Tajdari, H. Ghaffarian

Erschienen in: Soft Computing | Ausgabe 7/2018

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Abstract

This paper presents a novel Fuzzy-based bending angle predictor in laser bending process. Upon the case, and situations, different input data, membership functions and rules are developed dynamically for the Fuzzy predictor. Our main focus in developing the proposed controller is keeping generality of design. So, the controller can be adapted in different cases easily. To compensate for the possible lack of knowledge of experts for developing rule base of a Fuzzy controller, here, nonlinear regression is used as an alternative approach for developing the rule base. Furthermore, the performance of the controller is improved using particle swarm optimization (PSO) method. Also, based on the proposed Fuzzy controller and PSO, another predictor able to find one possible set of input values to catch a predefined angle is proposed. Several experimental tests were conducted to evaluate performance of the proposed controllers. Comparing experimental and predicted results shows that they are in a proper agreement with our claim.

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Titel: Input value prediction of parameters in laser bending using Fuzzy and PSO
verfasst von: M. R. Nejati
M. Hoseinpour Gollo
M. Tajdari
H. Ghaffarian
Publikationsdatum: 30.01.2017
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 7/2018
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-016-2479-1

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