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Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp016t053k319
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dc.contributor.authorCorrea Hernandez, Andres-
dc.contributor.authorGmachl, Claire F.-
dc.date.accessioned2024-03-20T16:18:56Z-
dc.date.available2024-03-20T16:18:56Z-
dc.date.created2023-07-24-
dc.date.issued2024-03-20-
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/dsp016t053k319-
dc.identifier.urihttps://doi.org/10.34770/bps9-7152-
dc.description.abstractThis item contains two files. A multi-layer perceptron (MLP) neural network is built using the MATLAB Deep Network Designer (.m file). It imports a quantum cascade laser (QCL) dataset and splits it into 70% training, 15% validation, and 15% testing subsets. The network consists of an input layer, three hidden layers (each having a normalization and activation layer), and a regression output layer. All of the layers are fully connected, and the root-mean-square error (RMSE) is used to evaluate the accuracy of the network. An algorithm is trained on the [-5, +20] QCL dataset using 50 neurons, ReLU activation function, solver Adam, 0.001 learning rate, over 50 epochs, and is saved to be used in the prediction of figure of merit values for QCL designs (.mat file).en_US
dc.description.tableofcontentsadam_relu_hidden_3_neuron_50_epoch_50_net.mat, mlp_reg_train.m, README.txten_US
dc.language.isoen_USen_US
dc.publisherPrinceton Universityen_US
dc.relationHttps://doi.org/10.34770/d644-0c85-
dc.rightsMIT Licenseen_US
dc.subjectquantum cascade laser, mid-infrared, figure of merit, machine learning, multi-layer perceptron, neural network, regressionen_US
dc.titleMLP neural network trained on the QCL [-5, +20] Å dataseten_US
dc.typeSoftwareen_US
Appears in Collections:EE Research Data Sets

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README.txt3.19 kBTextView/Download
adam_relu_hidden_3_neuron_50_epoch_50_net.mat28.21 kBUnknownView/Download
mlp_reg_train.m2.95 kBUnknownView/Download


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