A Reconfigurable Hardware for Fuzzy Controllers

Hardware for Soft Computing and Soft Computing for Hardware - Studies in Computational Intelligence ◽

10.1007/978-3-319-03110-1_6 ◽

2014 ◽

pp. 71-90

Author(s):

Nadia Nedjah ◽

Luiza de Macedo Mourelle

Keyword(s):

Reconfigurable Hardware ◽

Fuzzy Controllers

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Massively parallel scalable reconfigurable hardware for fuzzy controllers

2013 IEEE 4th Latin American Symposium on Circuits and Systems (LASCAS) ◽

10.1109/lascas.2013.6519019 ◽

2013 ◽

Author(s):

P. R. S. S. Sandres ◽

N. Nedjah ◽

L. de Macedo Mourelle

Keyword(s):

Reconfigurable Hardware ◽

Massively Parallel ◽

Fuzzy Controllers

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Synthesis and optimization of fuzzy controllers in the control systems of pyrolysis reactors

Collection of Scientific Publications NUS ◽

10.15589/jnn20170404 ◽

2017 ◽

Vol 0 (4) ◽

Author(s):

Oleksiy V. Kozlov ◽

Galyna V. Kondratenko ◽

Yuriy P. Kondratenko

Keyword(s):

Control Systems ◽

Fuzzy Controllers

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Fault Detection Methods for Interconnects in Reconfigurable Hardware

i-manager’s Journal on Embedded Systems ◽

10.26634/jes.2.4.2803 ◽

2014 ◽

Vol 2 (4) ◽

pp. 12-19

Author(s):

Pradeep C ◽

◽

Radhakrishnan R ◽

Keyword(s):

Fault Detection ◽

Reconfigurable Hardware ◽

Detection Methods

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Possibilities and Challenges for Reconfigurable Hardware and Cloud Architectures in Data-Intensive Scientific Applications

2020 Seventh International Conference on Software Defined Systems (SDS) ◽

10.1109/sds49854.2020.9143904 ◽

2020 ◽

Author(s):

Mohammed Bawatna ◽

Oliver Knodel ◽

Rainer G. Spallek

Keyword(s):

Reconfigurable Hardware ◽

Scientific Applications ◽

Data Intensive

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The Importance of Being X-Drop: High Performance Genome Alignment on Reconfigurable Hardware

2021 IEEE 29th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM) ◽

10.1109/fccm51124.2021.00023 ◽

2021 ◽

Author(s):

Alberto Zeni ◽

Guido Walter Di Donato ◽

Lorenzo Di Tucci ◽

Marco Rabozzi ◽

Marco D. Santambrogio

Keyword(s):

High Performance ◽

Reconfigurable Hardware ◽

Genome Alignment

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Optimal tuning of interval type-2 fuzzy controllers for nonlinear servo systems using Slime Mould Algorithm

International Journal of Systems Science ◽

10.1080/00207721.2021.1927236 ◽

2021 ◽

pp. 1-16

Author(s):

Radu-Emil Precup ◽

Radu-Codrut David ◽

Raul-Cristian Roman ◽

Alexandra-Iulia Szedlak-Stinean ◽

Emil M. Petriu

Keyword(s):

Servo Systems ◽

Fuzzy Controllers ◽

Slime Mould ◽

Optimal Tuning ◽

Interval Type

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Parallel real-time garbage collection of multiple heaps in reconfigurable hardware

ACM SIGPLAN Notices ◽

10.1145/2775049.2602996 ◽

2015 ◽

Vol 49 (11) ◽

pp. 117-127 ◽

Cited By ~ 1

Author(s):

David F. Bacon ◽

Perry Cheng ◽

Sunil Shukla

Keyword(s):

Real Time ◽

Garbage Collection ◽

Reconfigurable Hardware

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Design of Stable Fuzzy Controllers

2007 5th International Symposium on Intelligent Systems and Informatics ◽

10.1109/sisy.2007.4342646 ◽

2007 ◽

Cited By ~ 1

Author(s):

Dragan Z. Saletic

Keyword(s):

Fuzzy Controllers

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Modelling of Six-Phase Electric Drive with PI and PD Fuzzy Controllers

2021 IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream) ◽

10.1109/estream53087.2021.9431453 ◽

2021 ◽

Author(s):

Roma Rinkeviciene ◽

Brone Mitkiene ◽

Dainius Udris

Keyword(s):

Electric Drive ◽

Fuzzy Controllers

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Researches Regarding the Use of Fuzzy Controllers within CNC Feed Drives

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.772.229 ◽

2015 ◽

Vol 772 ◽

pp. 229-234

Author(s):

Radu Eugen Breaz ◽

Octavian Bologa

Keyword(s):

Machine Tool ◽

Fuzzy Controller ◽

Cnc Machine Tool ◽

Cnc Machine ◽

Fuzzy Controllers ◽

Feed Drive ◽

Feed Drives ◽

Simulation Based ◽

Position Controller ◽

Dc Servomotor

This paper presents some simulation based upon a dynamic model of a feed-drive within the structure of a CNC machine tool. A DC servomotor was considered as actuation device for the feed drive. For a given set of parameters for the position controller, two fuzzy types of fuzzy controllers were tested by means of simulation. The first fuzzy controller was a proportional one, with one input and one output, while the second one was a two variables one, with two inputs and one outputp.

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