A Model of Multi-Cascade Fuzzy Logic Controller Implemented Using Different Variations of Inference Algorithms

Cherniy, S.P.; Buzikayeva, A.V.; Gudim, A.S.

doi:10.1109/fareastcon.2019.8934872

Cited by 3 publications

(4 citation statements)

References 5 publications

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“…22 The design of the intelligent farm automatic organic irrigation system is realized by Fuzzy logic (FL) control, such as automatic opening of the electric pump reservoir and water storage status of power management from the reservoir to the farm irrigation system. 23 The neuro-fuzzy smith predictor (NFSP) is used to effectively control the distribution of water in major irrigation channels, which improves efficiency and reduces water loss. 24 Therefore, a neuro-fuzzy controller combined with the Smith predictor is designed, robust to the influence of external disturbances and changes in dynamic equipment parameters.…”

Section: Related Workmentioning

confidence: 99%

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Retracted: Adaptive neuro fuzzy inference system to enhance the classification performance in smart irrigation system

Vivekanandhan¹,

Sakthivel²,

Manikandan³

2021

Computational Intelligence

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Traditional agricultural systems require huge amounts of money for on‐site irrigation power. Irrigation is the process of giving water to plants for growth and development. The cost of global warming creates the potential effects of water adaptation measures to ensure food production and water availability for consumption. It requires continuous monitoring of soil moisture content at the root zone and initiating irrigation as per the pre‐programmed schedule depending upon the plant's nature, growth, soil type, and environment. Thus, the cost to farmers using conventional drip irrigation has become so high that they have to go manually and monitor the land frequently. So when the previous method was used for irrigation, it did not give correct classification results. Agricultural data mining technology provides the best crops for travel water to increase the number of crops produced. Agricultural data can be collected more efficiently using data collection and capable analytics sensors. This proposed work introduces an adaptive neuro‐fuzzy inference system (ANFIS) technique for analyzing agricultural plant growth based on soil, water level, temperature, and moisture conditions. The proposed technique has three phases: environment data collection using sensor node, preprocessing, feature selection, and classification. The initial phase captures the plant's environment condition via a sensor device, then transmits data to servers with the help of an edge node or gateway. This recursive adaptive filter method normalizes the data and removes irrelevant noise data from the sensor node. The feature selection method to extract the environment feature value with the help of a linear regression model. The ANFIS method's plant environment feature value has trained the neurons with the help of the stochastic gradient descent method. The classification method introduces a fuzzy rule to compute and validate the input parameters (e.g., soil moisture, temperature, and humidity) to predict any environment value changes. The comparison results prove that efficient monitoring is obtained through the proposed smart irrigation system.

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Section: Related Workmentioning

confidence: 99%

“…The design of the intelligent farm automatic organic irrigation system is realized by Fuzzy logic (FL) control, such as automatic opening of the electric pump reservoir and water storage status of power management from the reservoir to the farm irrigation system 23 …”

Section: Related Workmentioning

confidence: 99%

Retracted: Adaptive neuro fuzzy inference system to enhance the classification performance in smart irrigation system

Vivekanandhan¹,

Sakthivel²,

Manikandan³

2021

Computational Intelligence

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“…The vector-matrix description of the linear part [11] of the cyberphysical system control system can be presented below (for some cases, it can also include additional methods such as [12,13]).…”

Section: An Approach To the Intellectualization Of The Control Systemmentioning

confidence: 99%

“…In addition, due to the simultaneous availability of channel (12) with the archive of HS action patterns in the IM, it seems possible to detect anomalies in human behavior that are counterproductive, generating (causing) f MHS disturbance through channel (1), caused by the emotional component of the ES system HS. Thus, the DCIR-IM-EFS interaction can be eliminated, or the influence of f MHS significantly reduced, by eliminating the perturbation generator itself in HS via channel (12) or by promptly correcting the model in SCS and forming a new control law (channel (7)), correcting the state CS even before the triggering of its standard sensor system.…”

Section: An Approach To the Intellectualization Of The Control Systemmentioning

confidence: 99%

Improving Collaborative Robotic Complex Efficiency: An Approach to the Intellectualization of the Control System

Gorkavyy¹,

Ivanov²,

Sukhorukov³

et al. 2023

Intels’22

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Improving the Functioning of the Cybernetic Control System of the Mechatronic Module of the Robotic Complex

Gorkavyy¹,

Gorkavyy²,

Melnichenko³

et al. 2021

Informatics and Cybernetics in Intelligent Systems

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A Model of Multi-Cascade Fuzzy Logic Controller Implemented Using Different Variations of Inference Algorithms

Cited by 3 publications

References 5 publications

Retracted: Adaptive neuro fuzzy inference system to enhance the classification performance in smart irrigation system

Retracted: Adaptive neuro fuzzy inference system to enhance the classification performance in smart irrigation system

Improving Collaborative Robotic Complex Efficiency: An Approach to the Intellectualization of the Control System

Improving the Functioning of the Cybernetic Control System of the Mechatronic Module of the Robotic Complex

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