Study on Fuzzy-Neural Network for Inverted Plasma Arc Cutting Power Supply

Abstract:

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A variable interval fuzzy quantification algorithm with self-adjustable factor in full domain is proposed in this paper. It focuses on digital inverted plasma arc cutting power and studies strong nonlinearity and uncertainty of power. The neural network is also introduced to decouple cutting parameters variables in the multi-parameters coupling cutting process. This algorithm avoids complex nonlinear system modeling and realizes real-time and effective online control of cutting process by combining advantages of fuzzy control and neural network control. Furthermore, the optimized fuzzy control improves steady-state precision and dynamic performance of system simultaneously. The experimental result shows that this control improves precision, ripples, finish and other comprehensive index of work piece cut, and plasma arc cutting power supply based on fuzzy-neural network has excellent control performance.

Info:

Periodical:

Key Engineering Materials (Volumes 392-394)

Edited by:

Guanglin Wang, Huifeng Wang and Jun Liu

Pages:

735-742

DOI:

10.4028/www.scientific.net/KEM.392-394.735

Citation:

B. You et al., "Study on Fuzzy-Neural Network for Inverted Plasma Arc Cutting Power Supply", Key Engineering Materials, Vols. 392-394, pp. 735-742, 2009

Online since:

October 2008

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Price:

$35.00

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