Fuzzy systems, neural networks and neuro-fuzzy systems: A vision on their hardware implementation and platforms over two decades

Bosque, G.; Campo, I. del; Echanobe, Javier

doi:10.1016/j.engappai.2014.02.008

Cited by 68 publications

(26 citation statements)

References 204 publications

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“…Fuzzy logic controller (FLC) is pre-dominantly used in control of non-linear plants where mathematical modeling is difficult [1]. FPGAs have always been the preferred hardware platform for realization of FLC in lieu to its programmability [2]- [7].…”

Section: Introductionmentioning

confidence: 99%

Design and implementation of online fuzzy logic controller on FPGA

Maji

Jammu

Patra

et al. 2014

2014 Annual IEEE India Conference (INDICON)

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Intelligent controllers are widely used in industries to control non-linear plants, for which mathematical models are difficult to obtain. Fuzzy logic controllers (FLCs) are often used for the same purpose as they operate on linguistic rules and estimation of uncertainty, which represents the core essence of non-linear systems. However, realization of FLCs on hardware platform is quite an arduous task in lieu of it high complexity and number of input parameters. In this paper, we have implemented an online FLC which can be operated remotely on a Virtex V LX110T FPGA hardware. The proposed FLC hardware is fed with fuzzy parameters over Ethernet on the fly. This system is tested extensively for water level control of a tank.

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Section: Introductionmentioning

confidence: 99%

Design and implementation of online fuzzy logic controller on FPGA

Maji

Jammu

Patra

et al. 2014

2014 Annual IEEE India Conference (INDICON)

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“…Recent development of NFSs based on fault diagnosis can be found in Refs. [17,[115][116][117][118][119][120][121][122]. Nevertheless, several issues still need to be considered.…”

Section: Nfssmentioning

confidence: 99%

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Zhang

Jia

Zhu

et al. 2017

Chin. J. Mech. Eng.

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Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few comprehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition monitoring and fault diagnosis. The recent research and development of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are discussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mechanism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suitable method for a specific situation and pointing out potential research directions.

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“…The fuzzification is the transformation of numerical data from the input to linguistic terms. The knowledge base provides necessary information for all the components of the fuzzy controller [9][10][11]. The fuzzy inference engine or the logical decision-making is the core (brain) of the controller.…”

Section: Fuzzy Logic Controllermentioning

confidence: 99%

The Application of Fuzzy Control in Water Tank Level Using Arduino

Chabni¹,

Taleb²,

Benbouali³

et al. 2016

ijacsa

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Abstract-Fuzzy logic control has been successfully utilized in various industrial applications; it is generally used in complex control systems, such as chemical process control. Today, most of the fuzzy logic controls are still implemented on expensive highperformance processors. This paper analyzes the effectiveness of a fuzzy logic control using a low-cost controller applied to a water level control system. The paper also gives a low-cost hardware solution and practical procedure for system identification and control. First, the mathematical model of the process was obtained with the help of Matlab. Then two methods were used to control the system, PI (Proportional, Integral) and fuzzy control. Simulation and experimental results are presented.

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Fuzzy systems, neural networks and neuro-fuzzy systems: A vision on their hardware implementation and platforms over two decades

Cited by 68 publications

References 204 publications

Design and implementation of online fuzzy logic controller on FPGA

Design and implementation of online fuzzy logic controller on FPGA

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

The Application of Fuzzy Control in Water Tank Level Using Arduino

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