Adaptive Network-Based Fuzzy Inference System (ANFIS) Applied to Inverters: A Survey

Vargas, Oscar Sánchez; De León Aldaco, Susana Estefany; Alquicira, Jesús Aguayo; Vela-Valdés, Luis Gerardo; Núñez, Adolfo Rafael López

doi:10.1109/tpel.2023.3327014

Cited by 15 publications

(3 citation statements)

References 86 publications

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“…Additionally, ANFIS can efficiently handle numerical and linguistic data, enhancing its versatility in various applications compared to traditional ANNs. The adaptive nature of ANFIS allows it to continuously optimize its parameters, leading to improved accuracy and generalization performance, especially in scenarios with limited training data (Chuensiri et al 2024 ; Vargas et al 2024 ; Jazayeriy and Kazemitabar 2024 ).…”

Section: Methodsmentioning

confidence: 99%

Comparative assessment of deep belief network and hybrid adaptive neuro-fuzzy inference system model based on a meta-heuristic optimization algorithm for precise predictions of the potential evapotranspiration

Akiner,

Ghasri

2024

Environ Sci Pollut Res

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Accurately predicting potential evapotranspiration (PET) is crucial in water resource management, agricultural planning, and climate change studies. This research aims to investigate the performance of two machine learning methods, the adaptive network-based fuzzy inference system (ANFIS) and the deep belief network (DBN), in forecasting PET, as well as to explore the potential of hybridizing the ANFIS approach with the Snake Optimizer (ANFIS-SO) algorithm. The study utilized a comprehensive dataset spanning the period from 1983 to 2020. The ANFIS, ANFIS-SO, and DBN models were developed, and their performances were evaluated using statistical metrics, including R2, $${R}_{adj}^2$$ R adj 2 , NSE, WI, STD, and RMSE. The results showcase the exceptional performance of the DBN model, which achieved R2 and $${R}_{adj}^2$$ R adj 2 values of 0.99 and NSE and WI scores of 0.99 across the nine stations analyzed. In contrast, the standard ANFIS method exhibited relatively weaker performance, with R2 and $${R}_{adj}^2$$ R adj 2 values ranging from 0.52 to 0.88. However, the ANFIS-SO approach demonstrated a substantial improvement, with R2 and $${R}_{adj}^2$$ R adj 2 values ranging from 0.94 to 0.99, suggesting the value of optimization techniques in enhancing the model’s capabilities. The Taylor diagram and violin plots with box plots further corroborated the comparative analysis, highlighting the DBN model’s superior ability to closely match the observed standard deviation and correlation and its consistent and low-variance predictions. The ANFIS-SO method also exhibited enhanced performance in these visual representations, with an RMSE of 0.86, compared to 0.95 for the standard ANFIS. The insights gained from this study can inform the selection of the most appropriate modeling technique, whether it be the high-precision DBN, the flexible ANFIS, or the optimized ANFIS-SO approach, based on the specific requirements and constraints of the application.

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Section: Methodsmentioning

confidence: 99%

Comparative assessment of deep belief network and hybrid adaptive neuro-fuzzy inference system model based on a meta-heuristic optimization algorithm for precise predictions of the potential evapotranspiration

Akiner,

Ghasri

2024

Environ Sci Pollut Res

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“…Using fuzzy control principles and combining the advantages of both FLC and ANN, the neuro-fuzzy controller has many advantages, such as the learning capabilities of neural networks, parallel knowledge/data processing capabilities, and human fuzzy logic reasoning capabilities [87][88][89][90]. The ANFIS (adaptive neural network fuzzy inference system) is a fuzzy inference system based on the Takagi-Sugeno model [91]. The fundamental fuzzy control processes (i.e., fuzzification, fuzzy inference, and defuzzification) are implemented by neural networks [92].…”

Section: Adaptive Neuro-fuzzy Algorithm Optimizationmentioning

confidence: 99%

Control and Intelligent Optimization of a Photovoltaic (PV) Inverter System: A Review

Zhang,

Zhai,

Mao

et al. 2024

Energies

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PV power generation is developing fast in both centralized and distributed forms under the background of constructing a new power system with high penetration of renewable sources. However, the control performance and stability of the PV system is seriously affected by the interaction between PV internal control loops and the external power grid. The impact of the PV system on the reliability, stability, and power quality of power systems has restricted them to further participate in power supplies with a large capacity. Traditional control methods have become ineffective at dealing with these problems as the PV system becomes increasingly complex and nonlinear. Intelligent control as a more advanced technology has been integrated into the PV system to improve system control performance and stability. However, intelligent control for the PV system is still in the early stages due to the extensive calculation and intricate implementation of intelligent algorithms. Further investigations should be carried out to effectively combine intelligent control with the PV system to constitute an intelligent PV power system with multiple functions, high stability, and high-performance. This paper provides a systematic classification and detailed introduction of various intelligent optimization methods in a PV inverter system based on the traditional structure and typical control. The future trends and research topics are given to provide a reference for the intelligent optimization control in the PV system.

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“…That is, manipulators can accomplish the assigned task with high efficiency by simulating the learning ability of humans. Taking the neural network (NN) (Su et al, 2023a ; Wei and Jin, 2024 ) and fuzzy inference system (FIS) (Vargas et al, 2024 ) into account, both of them attempt to simulate the thinking and decision-making processes of humans in a certain way. Therefore, they have garnered the attention of researchers, and a lot of effort has been put into integrating them with manipulator control systems to improve the completion of the task and meet the requirements of different scenarios.…”

Section: Introductionmentioning

confidence: 99%

Online learning fuzzy echo state network with applications on redundant manipulators

Li,

Liu,

Gao

2024

Front. Neurorobot.

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Redundant manipulators are universally employed to save manpower and improve work efficiency in numerous areas. Nevertheless, the redundancy makes the inverse kinematics of manipulators hard to address, thus increasing the difficulty in instructing manipulators to perform a given task. To deal with this problem, an online learning fuzzy echo state network (OLFESN) is proposed in the first place, which is based upon an online learning echo state network and the Takagi–Sugeno–Kang fuzzy inference system (FIS). Then, an OLFESN-based control scheme is devised to implement the efficient control of redundant manipulators. Furthermore, simulations and experiments on redundant manipulators, covering UR5 and Franka Emika Panda manipulators, are carried out to verify the effectiveness of the proposed control scheme.

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Adaptive Network-Based Fuzzy Inference System (ANFIS) Applied to Inverters: A Survey

Cited by 15 publications

References 86 publications

Comparative assessment of deep belief network and hybrid adaptive neuro-fuzzy inference system model based on a meta-heuristic optimization algorithm for precise predictions of the potential evapotranspiration

Comparative assessment of deep belief network and hybrid adaptive neuro-fuzzy inference system model based on a meta-heuristic optimization algorithm for precise predictions of the potential evapotranspiration

Control and Intelligent Optimization of a Photovoltaic (PV) Inverter System: A Review

Online learning fuzzy echo state network with applications on redundant manipulators

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