PSO-ANFIS hybrid approach for prediction of wave reflection coefficient for semicircular breakwater

Kundapura, Suman; Hegde, Arkal Vittal

doi:10.1080/09715010.2018.1525688

Cited by 5 publications

(2 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Adaptive Network-based Fuzzy Inference System (ANFIS) classification is an artificial neural network based method, which is particularly used to solve pattern recognition problems that require rule-based process control (Sada and Ikpeseni 2021). These approaches are obtained after training fuzzy inference rules with artificial neural networks, which even experts in the field have difficulty in drawing conclusions (Kundapura and Hegde 2021;Yaseen et al 2019). The proposed method presents dynamic modification parameters of the ANFIS using the PSO algorithm.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Optimal ANFIS Parameters Tuning with Particle Swarm Optimization

Dirik¹,

Gül²

2021

European Journal of Science and Technology

View full text Add to dashboard Cite

This paper presents dynamic modification parameters of the Adaptive Neuro-Fuzzy Inference System (ANFIS) using the Particle Swarm Optimization (PSO) algorithm. In the proposed ANFIS_PSO, each particle dynamically adjusts its weight to the optimal states of the particles using a nonlinear fuzzy model. Tests of the model were performed using the "Signal-Time Series". The methods are tested simultaneously until the best method to solve the problem is found. The proposed model takes advantage of PSO to tune ANFIS parameters by minimizing mean square error (MSE), root mean square error (RMSE), R-Squared (R2) and Mean Absolute Error (MEA) metrics. The main contribution is a strategy for dynamically finding the best result, which identifies methods for solving a given problem using different performance metrics depending on the problem. The proposed structure's results were compared with several machine learning algorithms. Simulation results show the effectiveness of the proposed algorithm.

show abstract

Section: Introductionmentioning

confidence: 99%

Dynamic Optimal ANFIS Parameters Tuning with Particle Swarm Optimization

Dirik¹,

Gül²

2021

European Journal of Science and Technology

View full text Add to dashboard Cite

show abstract

“…However, real-time based parameter settings can be achieved by employing another highly efficient machine learning approach, PSO tuned adaptive neuro-fuzzy inference system (ANFIS) to improve the overall system stability. PSO tuned ANFIS models were implemented in various scenarios, such as viscosity estimation of an oil mixture [34] and predicting the wave reflection coefficient for semicircular breakwater considering a range of wave heights [35]. Therefore, in this paper, a PSO-tuned ANFIS approach was implemented to achieve real-time tuning of the PSS parameters.…”

Section: Introductionmentioning

confidence: 99%

Real-Time LFO Damping Enhancement in Electric Networks Employing PSO Optimized ANFIS

et al. 2020

View full text Add to dashboard Cite

In recent years, machine learning (ML) tools have gained tremendous momentum and received wide-spread attention in different segments of modern-day life. As part of digital transformation, the power system industry is one of the pioneers in adopting such attractive and efficient tools for various applications. Apparently, a nonthreatening, but slow-burning issue of the electric power systems is the low-frequency oscillations (LFO), which, if not dealt with appropriately and on time, could result in complete network failure. This paper addresses the role of a prominent ML family member, particle swarm optimization (PSO) tuned adaptive neuro-fuzzy inference system (ANFIS) for real-time enhancement of LFO damping in electric power system networks. It adopts and models two power system networks where in the first network, the synchronous machine is equipped with only a power system stabilizer (PSS), and in the other, the PSS of the synchronous machine is coordinated with the unified power flow controller (UPFC), a second-generation flexible alternating current transmission system (FACTS) device. Then, it develops the proposed ML approach to enhance LFO damping for both adopted networks based on the customary practices of statistical judgment. The performance measuring metrics of power system stability, including the minimum damping ratio (MDR), eigenvalue, and time-domain simulation, were used to analyze the developed approach. Moreover, the paper presents a comparative analysis and discussion with the referenced works’ achieved results to conclude the proposed PSO-ANFIS technique’s ability to enhance power system stability in real-time by damping out the unwanted LFO.

show abstract