Parallel computing-oriented method for long-time duration problem of force identification

Pan, Chudong; Deng, Xiongjie; Huang, Zhenjie

doi:10.1007/s00366-020-01097-6

Cited by 10 publications

(2 citation statements)

References 47 publications

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“…Then, contour-based recognition was used for recognition of whales and dolphins due to its time complexity and low identification rate [ 12 , 13 , 14 ]. The next item from the subset of intelligent methods is the ATR method based on soft computing [ 15 ], which is wildly popular due to its versatility and parallel structure [ 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

Classification of Marine Mammals Using the Trained Multilayer Perceptron Neural Network with the Whale Algorithm Developed with the Fuzzy System

Takhti

Saffari²,

Martín

et al. 2022

Computational Intelligence and Neuroscience

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The existence of various sounds from different natural and unnatural sources in the deep sea has caused the classification and identification of marine mammals intending to identify different endangered species to become one of the topics of interest for researchers and activist fields. In this paper, first, an experimental data set was created using a designed scenario. The whale optimization algorithm (WOA) is then used to train the multilayer perceptron neural network (MLP-NN). However, due to the large size of the data, the algorithm has not determined a clear boundary between the exploration and extraction phases. Next, to support this shortcoming, the fuzzy inference is used as a new approach to developing and upgrading WOA called FWOA. Fuzzy inference by setting FWOA control parameters can well define the boundary between the two phases of exploration and extraction. To measure the performance of the designed categorizer, in addition to using it to categorize benchmark datasets, five benchmarking algorithms CVOA, WOA, ChOA, BWO, and PGO were also used for MLPNN training. The measured criteria are concurrency speed, ability to avoid local optimization, and the classification rate. The simulation results on the obtained data set showed that, respectively, the classification rate in MLPFWOA, MLP-CVOA, MLP-WOA, MLP-ChOA, MLP-BWO, and MLP-PGO classifiers is equal to 94.98, 92.80, 91.34, 90.24, 89.04, and 88.10. As a result, MLP-FWOA performed better than other algorithms.

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Section: Introductionmentioning

confidence: 99%

Classification of Marine Mammals Using the Trained Multilayer Perceptron Neural Network with the Whale Algorithm Developed with the Fuzzy System

Takhti

Saffari²,

Martín

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Then, contourbased recognition was used to recognition whales and dolphins due to its time complexity and low identification rate 9 . The next item from the subset of intelligent methods is the ATR method based on soft computing, which is wildly popular due to its versatility and parallel structure 10,11 . MLP-NN neural network, due to its simple structure, high performance, and low computational complexity, has become a useful tool for automatically recognizing targets 12 .…”

Section: Introductionmentioning

confidence: 99%

Classification of Marine Mammals Using Trained Multilayer Perceptron Neural Network With Whale Algorithm Developed With Fuzzy System

Saffari¹,

Khishe²

2020

Preprint

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The existence of various sounds from different natural and unnatural sources in the deep sea has caused the classification and identification of marine mammals intending to identify different endangered species to become one of the topics of interest for researchers and activists field. In this paper, first, an experimental data set was created using a designed scenario. The Whale Optimization Algorithm (WOA) is then used to train the multilayer perceptron neural network (MLP-NN). However, due to the large size of the data, the algorithm has not determined a clear boundary between the exploration and extraction phases. Next, to support this shortcoming, the fuzzy inference is used as a new approach to developing and upgrading WOA called FWOA. Fuzzy inference By setting FWOA control parameters can well define the boundary between the two phases of exploration and extraction. To measure the performance of the designed categorizer, in addition to using it to categorize benchmark datasets, five benchmarking algorithms CVOA, WOA, ChOA, BWO, PGO, were also used for MLP-NN training. The measured criteria are concurrency speed, ability to avoid local optimization, and classification rate. The simulation results showed that FWOA has the highest classification accuracy in classifying both sets of marine mammal datasets and provides better results than the other five benchmark algorithms.

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Multiphysics model reduction of thermomechanical vibration in a state-space formulation

Ahn

Lee²,

Kim³

et al. 2022

Engineering with Computers

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Parallel computing-oriented method for long-time duration problem of force identification

Cited by 10 publications

References 47 publications

Classification of Marine Mammals Using the Trained Multilayer Perceptron Neural Network with the Whale Algorithm Developed with the Fuzzy System

Classification of Marine Mammals Using the Trained Multilayer Perceptron Neural Network with the Whale Algorithm Developed with the Fuzzy System

Classification of Marine Mammals Using Trained Multilayer Perceptron Neural Network With Whale Algorithm Developed With Fuzzy System

Multiphysics model reduction of thermomechanical vibration in a state-space formulation

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