Implementation of neuro-fuzzy system with modified high performance genetic algorithm on embedded systems

Nasrollahzadeh, Ali; Karimian, Ghader; Mehrafsa, Amir

doi:10.1016/j.asoc.2017.07.007

Cited by 7 publications

(5 citation statements)

References 24 publications

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“…The mean value of speedup was about 10.89× in Msps and 30.89× in Mflips (see Table 7) and this results are very expressive to big data and MMD applications [1,2,3]. High-throughput fuzzy controllers are also important to speed control systems such as tactile internet applications [22,21].…”

Section: Analysis Of the Comparisonmentioning

confidence: 84%

“…Systems based on Fuzzy Logic (FL), have been used in many industrial and commercial applications such as robotics, automation, control and classification problems. Unlike high data volume systems, such as Big Data and Mining of Massive Datasets (MMD) [1,2,3], one of the great advantages of Fuzzy Logic is its ability to work with incomplete or inaccurate information.…”

Section: Introductionmentioning

confidence: 99%

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Proposal of Takagi–Sugeno Fuzzy-PI Controller Hardware

Silva

Lopes

Valderrama

et al. 2020

Sensors

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This work proposes dedicated hardware for an intelligent control system on Field Programmable Gate Array (FPGA). The intelligent system is represented as Takagi-Sugeno Fuzzy-PI controller. The implementation uses a fully parallel strategy associated with a hybrid bit format scheme (fixed-point and other floatingpoint). Two hardware designs are proposed; the first one uses a single clock cycle processing architecture, and the other uses a pipeline scheme. The bit accuracy was tested by simulation with a non linear control system of robotic manipulator. The area, throughput, and dynamic power consumption of the implemented hardware are used to validate and compare the results of this proposal. The results achieved allow that the proposal hardware can use in several applications with high-throughput, low-power and ultra-low-latency restrictions such as teleportation of robot manipulators, tactile internet, industrial automation in industry 4.0, and others. such as the tactile Internet [21,22], the Internet of Things (IoT) and Industry 4.0, where the problems associated with processing, power, latency and miniaturization are fundamental. Robotic manipulators used on tactile internet need a high-throughput and ultra-low-latency control system, and this can be achieved with dedicated hardware [21].The development of dedicated hardware, in addition to speeding up parallel processing, makes it possible to operate with clocks adapted to low-power consumption [23,24,25,26,27,28,29]. The works presented in [30,31,32,33,34,35,36,37] propose implementations of FS on reconfigurable hardware (Field Programmable Gate Array -FPGA), showing the possibilities associated with the acceleration of fuzzy inference processes having a high degree of parallelization. Other works propose specific implementations of Fuzzy Control Systems (FCS) using the Fuzzy Mamdani Inference Machine (M-FIM) and the Takagi-Sugeno Fuzzy Inference propose the Takagi-Sugeno hardware acceleration for other types of application fields. This work aims to develop a new hardware proposal for a Fuzzy-PI controller with TS-FIM. Unlike most of the works presented, this project offers a fully parallel scheme associated with a hybrid platform using fixed-point and floating-point representations. Two TS-FIM hardware modules have been proposed, the first (here called TS-FIM module one-shot) takes one sample time to execute the TS-FIM, and the second (here called as TS-FIM module pipeline) uses registers inside the TS-FIM. Two Fuzzy-PI controller hardware have been proposed, one for the TS-FIM one-shot module and another for the TS-FIM module pipeline. The proposed hardware have been implemented, tested and validated on a Xilinx Virtex 6 FPGA. The synthesis results, in terms of size, resources and throughput, are presented according to the number of bits and the type of numerical precision. Already, the physical area on the target FPGA reaches less than 7%. The implementation achieved a throughput between 10 and 18Msps (Mega samples per second), and between 490and 882 Mfl...

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Section: Analysis Of the Comparisonmentioning

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Proposal of Takagi–Sugeno Fuzzy-PI Controller Hardware

Silva

Lopes

Valderrama

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…They are extensively used in consumer electronic devices such as smartphones and tablets. The implementation of parallel evolutionary algorithms on embedded systems based on single-core and multi-core ARM processors starts to be explored [40].…”

Section: Desktop-gridmentioning

confidence: 99%

A unified view of parallel multi-objective evolutionary algorithms

Talbi

2019

Journal of Parallel and Distributed Computing

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“…To decrease software experiment execution time, one can use faster hardware or optimize underlining algorithms. Some hardware options to decrease execution time include FPGA [49,50], GPU [51], faster processors [52] or computer clusters [53,54]. Algorithm optimization examples are found in studies by Gou et al [55], Naderi et al [56] and Sánchez-Oro et al [57].…”

Section: Computer Clustermentioning

confidence: 99%

Parallelism Strategies for Big Data Delayed Transfer Entropy Evaluation

2019

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Generated and collected data have been rising with the popularization of technologies such as Internet of Things, social media, and smartphone, leading big data term creation. One class of big data hidden information is causality. Among the tools to infer causal relationships, there is Delay Transfer Entropy (DTE); however, it has a high demanding processing power. Many approaches were proposed to overcome DTE performance issues such as GPU and FPGA implementations. Our study compared different parallel strategies to calculate DTE from big data series using a heterogeneous Beowulf cluster. Task Parallelism was significantly faster in comparison to Data Parallelism. With big data trend in sight, these results may enable bigger datasets analysis or better statistical evidence.

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Implementation of neuro-fuzzy system with modified high performance genetic algorithm on embedded systems

Cited by 7 publications

References 24 publications

Proposal of Takagi–Sugeno Fuzzy-PI Controller Hardware

Proposal of Takagi–Sugeno Fuzzy-PI Controller Hardware

A unified view of parallel multi-objective evolutionary algorithms

Parallelism Strategies for Big Data Delayed Transfer Entropy Evaluation

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