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Wyszukujesz frazę "search algorithm" wg kryterium: Temat


Wyświetlanie 1-3 z 3
Tytuł:
Optimization of the Morphological Parameters of a Metal Foam for the Highest Sound Absorption Coefficient Using Local Search Algorithm
Autorzy:
Jafari, Mohammad Javad
Khavanin, Ali
Ebadzadeh, Touraj
Fazlali, Mahmood
Sharak, Mohsen Niknam
Madvari, Rohollah Fallah
Powiązania:
https://bibliotekanauki.pl/articles/176536.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
SAC
Sound Absorption Coefficient
LSA
Local Search Algorithm
metal foam
optimization
Opis:
Due to its unique features, the metal foam is considered as one of the newest acoustic absorbents. It is a navel approach determining the structural properties of sound absorbent to predict its acoustical behavior. Unfortunately, direct measurements of these parameters are often difficult. Currently, there have been acoustic models showing the relationship between absorbent morphology and sound absorption coefficient (SAC). By optimizing the effective parameters on the SAC, the maximum SAC at each frequency can be obtained. In this study, using the Benchmarking method, the model presented by Lu was validated in MATLAB coding software. Then, the local search algorithm (LSA) method was used to optimize the metal foam morphology parameters. The optimized parameters had three factors, including porosity, pore size, and metal foam pore opening size. The optimization was applied to a broad band of frequency ranging from 500 to 8000 Hz. The predicted values were in accordance with benchmark data resulted from Lu model. The optimal range of the parameters including porosity of 50 to 95%, pore size of 0.09 to 4.55 mm, and pore opening size of 0.06 to 0.4 mm were applied to obtain the highest SAC for the frequency range of 500 to 800 Hz. The optimal amount of pore opening size was 0.1 mm in most frequencies to have the highest SAC. It was concluded that the proposed method of the LSA could optimize the parameters affecting the SAC according to the Lu model. The presented method can be a reliable guide for optimizing microstructure parameters of metal foam to increase the SAC at any frequency and can be used to make optimized metal foam.
Źródło:
Archives of Acoustics; 2020, 45, 3; 487-497
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Real-time validation of an automatic generation control system considering HPA-ISE with crow search algorithm optimized cascade FOPDN-FOPIDN controller
Autorzy:
Babu, Naladi Ram
Chiranjeevi, Tirumalasetty
Devarapalli, Ramesh
Knypiński, Łukasz
Garcìa Màrquez, Fausto Pedro
Powiązania:
https://bibliotekanauki.pl/articles/27312009.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
crow search algorithm
dish-stirling solar system
AGC
RT Lab
FOPDN-FOPIDN controller
Opis:
This article validates the application of RT-Lab for the AGC studies of three-area systems. All the areas are employed with thermal-DSTS systems. A new controller named cascade FOPDN-FOPPIDN is employed. Its parameters are optimized using a CSA, subjecting to a new PI named HPA-ISE. The responses of the FOPDN-FOPIDN controller are related and are superior over PIDN and TIDN controllers. Moreover, the dominance of HPA-ISE is verified with ISE, and it performs better in terms of system dynamics. Further, the system performance reliability is analyzed with the AC-HVDC and is better than the AC system. Besides, sensitivity analysis recommends that the proposed FOPDN-FOPIDN at diverse conditions is robust and more reliability.
Źródło:
Archives of Control Sciences; 2023, 33, 2; 371--390
1230-2384
Pojawia się w:
Archives of Control Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Multi-Layer Perceptron Neural Network Utilizing Adaptive Best-Mass Gravitational Search Algorithm to Classify Sonar Dataset
Autorzy:
Mosavi, Mohammad Reza
Khishe, Mohammad
Naseri, Mohammad Jafar
Parvizi, Gholam Reza
Ayat, Mehdi
Powiązania:
https://bibliotekanauki.pl/articles/176971.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
MLP NN
Multi-Layer Perceptron Neural Network
ABGSA
Adaptive Best Mass Gravitational Search Algorithm
sonar
classification
Opis:
In this paper, a new Multi-Layer Perceptron Neural Network (MLP NN) classifier is proposed for classifying sonar targets and non-targets from the acoustic backscattered signals. Besides the capabilities of MLP NNs, it uses Back Propagation (BP) and Gradient Descent (GD) for training; therefore, MLP NNs face with not only impertinent classification accuracy but also getting stuck in local minima as well as lowconvergence speed. To lift defections, this study uses Adaptive Best Mass Gravitational Search Algorithm (ABGSA) to train MLP NN. This algorithm develops marginal disadvantage of the GSA using the bestcollected masses within iterations and expediting exploitation phase. To test the proposed classifier, this algorithm along with the GSA, GD, GA, PSO and compound method (PSOGSA) via three datasets in various dimensions will be assessed. Assessed metrics include convergence speed, fail probability in local minimum and classification accuracy. Finally, as a practical application assumed network classifies sonar dataset. This dataset consists of the backscattered echoes from six different objects: four targets and two non-targets. Results indicate that the new classifier proposes better output in terms of aforementioned criteria than whole proposed benchmarks.
Źródło:
Archives of Acoustics; 2019, 44, 1; 137-151
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-3 z 3

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