2007
DOI: 10.1080/00207210701464634
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Artificial neural networks for temperature dependent noise modeling of microwave transistors

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Cited by 4 publications
(1 citation statement)
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“…Here, x t is a vector of input parameters, f rt d is the desired model output for x t at its inputs and N T is a total number of testing data samples. For all the samples x t from the test set, the model output is found and average test error (ATE [%]), worst-case error (WCE [%]), and Pearson Product-Moment correlation coefficient (r PPM ) [4,10] are computed. Pearson ProductMoment correlation coefficient between the desired (referent) and the modeled data is defined as …”
Section: Combined Kbn-mlp Model Of Loaded Cavitymentioning
confidence: 99%
“…Here, x t is a vector of input parameters, f rt d is the desired model output for x t at its inputs and N T is a total number of testing data samples. For all the samples x t from the test set, the model output is found and average test error (ATE [%]), worst-case error (WCE [%]), and Pearson Product-Moment correlation coefficient (r PPM ) [4,10] are computed. Pearson ProductMoment correlation coefficient between the desired (referent) and the modeled data is defined as …”
Section: Combined Kbn-mlp Model Of Loaded Cavitymentioning
confidence: 99%