mlp matlab trained network parameters Search Results


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mlp matlab trained network parameters  (MathWorks Inc)
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mlp neural network  (MathWorks Inc)
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Implemented <t>MLP</t> <t>neural</t> network.
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matlab 2018b software  (MathWorks Inc)
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maximum-likelihood adaptive procedure105  (MathWorks Inc)
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matlab 8.4 software  (MathWorks Inc)
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maximum likelihood procedure mlp  (MathWorks Inc)
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Image Search Results


Implemented MLP neural network.

Journal: PLOS ONE

Article Title: Application of MLP neural network to predict X-ray spectrum from tube voltage, filter material, and filter thickness used in medical imaging systems

doi: 10.1371/journal.pone.0294080

Figure Lengend Snippet: Implemented MLP neural network.

Article Snippet: MATLAB was used to train and evaluate the MLP neural network used in this investigation.

Techniques:

Comparison of spectra reconstructed by MLP neural network and the original spectrum, a) Be filter with thickness of 1.2 mm and tube voltage of 50kV, b) Al filter with thickness of 0.6 mm and tube voltage of 80kV, c) Be filter with thickness of 2 mm and tube voltage of 150kV, d) Al filter with thickness of 1.6 mm and tube voltage of 100kV, e) Al filter with thickness of 1.2 mm and tube voltage of 20kV, and f) Be filter with thickness of 0.4 mm and tube voltage of 30kV.

Journal: PLOS ONE

Article Title: Application of MLP neural network to predict X-ray spectrum from tube voltage, filter material, and filter thickness used in medical imaging systems

doi: 10.1371/journal.pone.0294080

Figure Lengend Snippet: Comparison of spectra reconstructed by MLP neural network and the original spectrum, a) Be filter with thickness of 1.2 mm and tube voltage of 50kV, b) Al filter with thickness of 0.6 mm and tube voltage of 80kV, c) Be filter with thickness of 2 mm and tube voltage of 150kV, d) Al filter with thickness of 1.6 mm and tube voltage of 100kV, e) Al filter with thickness of 1.2 mm and tube voltage of 20kV, and f) Be filter with thickness of 0.4 mm and tube voltage of 30kV.

Article Snippet: MATLAB was used to train and evaluate the MLP neural network used in this investigation.

Techniques: Comparison