Phase space elliptic density feature for epileptic EEG signals classification using metaheuristic optimization method

2020 ◽  
Vol 205 ◽  
pp. 106276 ◽  
Author(s):  
Nastaran Darjani ◽  
Hesam Omranpour
Keyword(s):  
Phase Space ◽  
Optimization Method ◽  
Eeg Signals ◽  
Metaheuristic Optimization

scholarly journals Harmonic Elimination of Cascaded Multilevel Inverter using Metaheuristic Optimization Methods

2018 ◽  
Vol 7 (4.15) ◽  
pp. 272 ◽  
Author(s):  
Srikanta Kumar Dash ◽  
Byamakesh Nayak ◽  
Jiban Ballav Sahu ◽  
Rojalin Rout
Keyword(s):  
Harmonic Distortion ◽  
Optimization Methods ◽  
Optimization Method ◽  
Multilevel Inverter ◽  
Computational Time ◽  
Local Minima ◽  
Metaheuristic Optimization ◽  
Harmonic Elimination ◽  
Whale Optimization ◽  
Cascaded Multilevel Inverter

The harmonic elimination of multilevel inverters is a complicated task that includes nonlinear transcendental equations. With the increase in the level of the multilevel inverter, the no of variables of the equation also increases which makes the problem more complicated. Metaheuristic optimization algorithms play an important role in finding out optimum switching angles required for elimination of harmonics in a lesser computational time avoiding multiple local minima. This paper deals with the harmonic elimination of cascaded multilevel inverter using whale optimization algorithm. The whale optimization method has the ability to escape local minima and it takes less time of computation of results. Results are verified theoretically by taking an example of a 15-level cascaded H-bridge inverter fed from equal d.c.sources. The above scheme well minimizes lower order harmonics and gives better output voltage and a low total harmonic distortion.  

A novel hybrid metaheuristic optimization method: hypercube natural aggregation algorithm

2019 ◽  
Vol 24 (12) ◽  
pp. 8823-8856 ◽  
Author(s):  
Oscar Maciel ◽  
Arturo Valdivia ◽  
Diego Oliva ◽  
Erik Cuevas ◽  
Daniel Zaldívar ◽  
...  
Keyword(s):  
Optimization Method ◽  
Hybrid Metaheuristic ◽  
Metaheuristic Optimization

scholarly journals Black-Hole Optimization Applied to the Parametric Estimation in Distribution Transformers Considering Voltage and Current Measures

Computers ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 124
Author(s):  
Camilo Andres Arenas-Acuña ◽  
Jonathan Andres Rodriguez-Contreras ◽  
Oscar Danilo Montoya ◽  
Edwin Rivas-Trujillo
Keyword(s):  
Black Hole ◽  
Programming Model ◽  
Estimation Error ◽  
Optimization Technique ◽  
Optimization Method ◽  
Point Of View ◽  
Parametric Estimation ◽  
Metaheuristic Optimization ◽  
Distribution Transformers ◽  
Matlab Programming

The problem of parametric estimation in single-phase transformers is addressed in this research from the point of view of metaheuristic optimization. The parameters of interest are the series resistance and reactance as well as the magnetization resistance and reactance. To obtain these parameters considering only the voltage and the currents measured in the terminals of the transformer, a nonlinear optimization model that deals with the minimization of the mean square error among the measured and calculated voltage and current variables is formulated. The nonlinear programming model is solved through the implementation of a simple but efficient metaheuristic optimization technique known as the black-hole optimizer. Numerical simulations demonstrate that the proposed optimization method allows for the reduction in the estimation error among the measured and calculated variables when compared with methods that are well established in the literature such as particle swarm optimization and genetic algorithms, among others. All the simulations were carried out in the MATLAB programming environment.

scholarly journals Higher-Order Phase-Space Reconstruction for Detection of Epileptic Electroencephalogram

2021 ◽  
Vol 12 (2) ◽  
pp. 2533-2539
Author(s):  
Nazia Parveen, Et. al.
Keyword(s):  
Phase Space ◽  
Euclidean Distance ◽  
Higher Order ◽  
Phase Space Reconstruction ◽  
Intrinsic Mode Functions ◽  
Eeg Signals ◽  
Novel Technique ◽  
Reconstructed Phase Space ◽  
A New Technique

In this paper, the authors propose a new technique for the classification of seizures, non-seizures, and seizure-free EEG signals based on non-linear trajectories of EEG signals. The EEG signals are decomposed using the EMD technique to obtain intrinsic mode functions (IMFs). The phase space of these IMFs is then reconstructed using a novel technique of higher-order dimensions (3D, 4D, 5D, 6D, 7D, 8D, 9D, and 10D). The existing techniques of seizure detection have deployed 2D & 3D phase–space reconstruction only. The Euclidean distance of all higher-order PSR is used as a feature to classify seizures, non-seizures, and seizure-free EEG signals. The performance of the proposed method is analyzed on the Bonn University database in which 7D reconstructed phase space classification accuracy of 99.9% has been achieved both using Random Forest classifier and J48 decision tree.

Depression recognition based on the reconstruction of phase space of EEG signals and geometrical features

2021 ◽  
Vol 179 ◽  
pp. 108078
Author(s):  
Hesam Akbari ◽  
Muhammad Tariq Sadiq ◽  
Ateeq Ur Rehman ◽  
Mahdieh Ghazvini ◽  
Rizwan Ali Naqvi ◽  
...  
Keyword(s):  
Phase Space ◽  
Eeg Signals ◽  
Geometrical Features
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