A spintronic memristor crossbar array for fuzzy control with application in the water valves control system

Liu, Jun; Xie, Shaojun; Chen, Chong; Xie, Dan; Yang, Mei

doi:10.1177/0020294019838594

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…Fuzzy control generally deals with control of complex non-linear systems, which are hard to express by accurate mathematical models. 40,41 FIS is a linguistic instruction-oriented system which is composed of fuzzification, fuzzy decision-making (conjunction of the antecedent propositions refers to their supporting degrees and implication of consequent results), aggregation of the results in a output set and defuzzification of the output fuzzy set ( Figure 5). Outputs of the FIS systems command both the V p and the V m .…”

Section: Fuzzy Control Methodsmentioning

confidence: 99%

Modeling and simulation of a fuzzy heat distribution controlled high-voltage DC resistive divider

Yılmaz

Kilci

2020

Measurement and Control

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In order to improve quality in manufacturing, the measuring instruments used in production process should regularly be monitored and corrected according to international or national standards. Calibration of high-voltage equipment and precise measurements of DC high voltages are accomplished by standard voltage divider. Self-heating effect is the main error source of measurement in high-voltage DC resistive dividers. Therefore, precise control systems should be designed to keep stability of the ambient temperature and to regulate the heat distribution along the high-voltage DC resistive divider. For this purpose, a heat controlled resistive divider whose input voltage ( Vin) is up to 5 kV was designed. This study is focused on heat convention and the dissipation model of the resistive divider and executes some control simulations under various conditions that aim to find the appropriate control method. Responses of the high-voltage DC resistive divider model are compared with and are validated by the responses of the designed actual system. The model provides us faster analyze and design solutions for novel methods. In this way, analyzing and controlling higher voltage dividers, such as 100 KV, will reduce just into a parameter change on the model. The fuzzy control method is suggested since the system dynamic has non-linear characteristics. Fuzzy temperature difference controller keeps temperature at a certain degree where fuzzy vertical temperature gradient controller keeps vertical temperature gradient around zero. Actual system and model responses for the fuzzy control are compared and interpreted.

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Section: Fuzzy Control Methodsmentioning

confidence: 99%

Modeling and simulation of a fuzzy heat distribution controlled high-voltage DC resistive divider

Yılmaz

Kilci

2020

Measurement and Control

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“…The challenge brought by data computation means that data sampling is indispensable, and it is critical to determine which sampling methods to choose. 4 Due to the overall characteristics represented by the subsets of data, the performance of data analysis can be ensured.…”

Section: Introductionmentioning

confidence: 99%

Granular data modeling and analysis based on optimized subsets of data

Dong

Wang

et al. 2022

Measurement and Control

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The study proposes a way of developing granular models based on optimized subsets of data with different sampling sizes, in which three generally used models, namely Support Vector Machine, K-Nearest Neighbor, and Long Short-Term Memory, are designed and transformed into granular version for achieving a good performance with sufficient functionality. First, a collection of subsets are determined using different sampling methods, which are subsequently applied to play as an essential prerequisite of the proposed models. Then, the principle of justifiable granularity is utilized to the design of interval information granules based on the subsets of data. The design process is associated with a well-defined optimization problem realized by achieving a sound compromise between two conflicting criteria: coverage and specificity. To evaluate the performance of the granular models, two aspects are considered: (i) sampling methods used in determining suitable subsets of data; (ii) different models applied to be transformed into granular models. A series of experimental studies are conducted to verify the feasibility of the proposed granular models.

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“…Memristor, whose unique advantages are automatic successive memory and nonvolatility, brought new opportunity for solving the key question of fuzzy control. With the design idea of software harden, that was the research that constructed membership function in the fuzzy controller based on the unique feature of crossbar array of the spintronic memristor and elaborated the whole construction process [16]. Starting with looking for the river bed, the front DC motor raises or lowers the infrared sensor with the FLC algorithm.…”

Section: Introductionmentioning

confidence: 99%

Hybrid FLC-LMS Algorithm for Predicting Sediment Volume in the River

Prasetyowati¹

2021

IJIES

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Problems such as flooding, various diseases from microbial pathogens, rivers becoming slum and unsightly, reduced availability: The data found that 82% of the 550 rivers in Indonesia were polluted and in critical condition. The high level of pollution caused various of clean water and many more. Sediment was one of the causes of that problem. There have been many studies detecting objects in the water, but no one predicted the volume of objects in the water. The purpose of this research is to make a device to detect the volume of sediment in the rivers to improve the performance of river monitoring. A device was equipped with an infrared sensor. Raising and lowering the sensor under the device was controlled by the front DC motor with a Fuzzy Logic Controller (FLC) algorithm using two input and one output. Error (E) and change of error (CE) were inputs for FLC and change of control was the output. The fuzzy membership function depended on the distance of the sensor to the sediment. The number of membership functions used was less than the previous research, so the calculation was much simple. This research used hybrid FLC and Adaptive LMS predictive algorithm. Fuzzy Logic Controller was for controlling the Front DC motor when raising and lowering the sensor and Adaptive LMS predictive algorithm was for predicting the volume and minimalizing the error calculation. PID control was also used as a comparison. The results showed that using the FLC algorithm can find sediment in less than four seconds with an error rate of 0.0005, while the PID sensor found sediment in more than 4 seconds with an error rate of 0.001. In addition, the Adaptive LMS predictive algorithm minimized the error calculation with an accuracy level of 99.66 percent.

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A spintronic memristor crossbar array for fuzzy control with application in the water valves control system

Cited by 5 publications

References 18 publications

Modeling and simulation of a fuzzy heat distribution controlled high-voltage DC resistive divider

Modeling and simulation of a fuzzy heat distribution controlled high-voltage DC resistive divider

Granular data modeling and analysis based on optimized subsets of data

Hybrid FLC-LMS Algorithm for Predicting Sediment Volume in the River

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