ANFIS-Based Climate Controller for Computerized Greenhouse System

Oubehar, H.; Selmani, A.; Ed-Dahhak, Abdelali; Lachhab, Abdeslam; Archidi, M.E.; Bouchikhi, Benachir

doi:10.25046/aj050102

Cited by 11 publications

(8 citation statements)

References 10 publications

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“…The neuro-fuzzy controller was used in [8] to ensure that one leg of a quadruped robot follows the desired trajectory. In [9], the neuro-fuzzy controller was used to stabilize the climatic variables inside greenhouse system at required levels for crops development. An adaptive neuro-fuzzy controller was designed in [10], for the speed control of permanent magnet synchronous motor.…”

Section: Proceedings Of Engineering and Technologymentioning

confidence: 99%

Tractor-Implement Tillage Depth Control Using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Timene

Ngasop

Djalo

2021

Proc. eng. technol. innov.

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This study presents a design of an adaptive neuro-fuzzy controller for tractors’ tillage operations. Since the classical controllers allows plowing depth errors due to the variations of lands structure, the use of the combined neural networks and fuzzy logic methods decreases these errors. The proposed controller is based on Adaptive Neuro-Fuzzy Inference System (ANFIS), which permits the generation of fuzzy rules to cancel the nonlinearity and disturbances on the implement. The design and simulations of the system, which consist of a hitch-implement mechanism, an electro-hydraulic actuator, and a neuro-fuzzy controller, are conducted in SolidWorks and MATLAB software. The performance of the proposed controller is analyzed and is contrasted with a Proportional Integral Derivative (PID) controller. The obtained results show that the neuro-fuzzy controller adapts perfectly to the dynamics of the system with rejection of disturbances.

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Section: Proceedings Of Engineering and Technologymentioning

confidence: 99%

Tractor-Implement Tillage Depth Control Using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Timene

Ngasop

Djalo

2021

Proc. eng. technol. innov.

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“…Adaptive Neuro-Fuzzy Inference System (ANFIS) is a more recent intelligent supervised hybrid machine learning technique that solves classification and modeling problems by combining the learning capabilities of ANNs and fuzzy logic systems [ 36 ]. Shastry and Sanjay [ 37 ] describe the fundamental concept and architecture of ANFIS, as well as some of the applications of ANFIS in agriculture.…”

Section: Introductionmentioning

confidence: 99%

Application of image processing and soft computing strategies for non-destructive estimation of plum leaf area

et al. 2022

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Plant leaf area (LA) is a key metric in plant monitoring programs. Machine learning methods were used in this study to estimate the LA of four plum genotypes, including three greengage genotypes (Prunus domestica [subsp. italica var. claudiana.]) and a single myrobalan plum (prunus ceracifera), using leaf length (L) and width (W) values. To develop reliable models, 5548 leaves were subjected to experiments in two different years, 2019 and 2021. Image processing technique was used to extract dimensional leaf features, which were then fed into Linear Multivariate Regression (LMR), Support Vector Regression (SVR), Artificial Neural Networks (ANN), and the Adaptive Neuro-Fuzzy Inference System (ANFIS). Model evaluation on 2019 data revealed that the LMR structure LA = 0.007+0.687 L×W was the most accurate among the various LMR structures, with R2 = 0.9955 and Root Mean Squared Error (RMSE) = 0.404. In this case, the linear kernel-based SVR yielded an R2 of 0.9955 and an RMSE of 0.4871. The ANN (R2 = 0.9969; RMSE = 0.3420) and ANFIS (R2 = 0.9971; RMSE = 0.3240) models demonstrated greater accuracy than the LMR and SVR models. Evaluating the models mentioned above on data from various genotypes in 2021 proved their applicability for estimating LA with high accuracy in subsequent years. In another research segment, LA prediction models were developed using data from 2021, and evaluations demonstrated the superior performance of ANN and ANFIS compared to LMR and SVR models. ANFIS, ANN, LMR, and SVR exhibited R2 values of 0.9971, 0.9969, 0.9950, and 0.9948, respectively. It was concluded that by combining image analysis and modeling through ANFIS, a highly accurate smart non-destructive LA measurement system could be developed.

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“…In response, artificial intelligence (AI) techniques are introduced as a solution to these problems. The main theory of AI is fuzzy logic and artificial neural network (ANN) [11]. Fuzzy logic has been designed and is successful in controlling non-linear systems.…”

Section: Introductionmentioning

confidence: 99%

“…Artificial neural networks (ANN) are useful in learning abilities and predicting, while fuzzy logic is useful in reasoning based on existing rules or producing expert systems by itself [10]. To utilize the second function of these controls, ANFIS algorithms (Adaptive Neuro-Fuzzy Inference System) is used to overcome the complexity control, the dynamics and the non-linearity systems [11].…”

Section: Introductionmentioning

confidence: 99%

High Accuracy Electric Water Heater using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Sudiharto

DwiMurdianto²,

Budikarso³

et al. 2022

KINETIK

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Nowadays, water heater is a common household appliance. Water heater can be divided into three types, based on fuel sources: gas, diesel, and electric. Electric water heater is the most common due to its ease of use. The problems that often occur on electric water heater are over-temperature due to user error in setting up the thermostat and inaccurate readings caused by a conventional system control. These problems will cause a surge in power consumption. Over-temperature and conventional control inaccuracies can be overcome using the Artificial Intelligence (AI) control algorithm in the form of an adaptive neuro-fuzzy inference system (ANFIS). The proposed algorithm acts as a control by maintaining the stability of the temperature to obtain more accurate results. An accurate temperature reading can lower power consumption in electric water heater. This study tries to simulate Electric Water Heater temperature control using the ANFIS algorithm until stable readings can be achieved in all temperature settings. Results from disturbance tests in the form of external condition that causes sudden temperature change show that the system can maintain stability with an average error margin of 0.045% and the rate of accuracy of 99.955%.

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ANFIS-Based Climate Controller for Computerized Greenhouse System

Cited by 11 publications

References 10 publications

Tractor-Implement Tillage Depth Control Using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Tractor-Implement Tillage Depth Control Using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Application of image processing and soft computing strategies for non-destructive estimation of plum leaf area

High Accuracy Electric Water Heater using Adaptive Neuro-Fuzzy Inference System (ANFIS)

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