{
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    "title": "Forecasting the Dielectric Properties of the Insulation Material using an Embedded ANN Microcontroller",
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    "abstract": "This paper describes a laboratory application for the forecasting the dielectric properties, using an artificial neural network embedded in a microprocessor. The dielectric permittivity and loss factor of the insulation material were determined as a function of temperature and frequency inputs by an implemented ANN model. For this purpose, a three-layer feed-forward back-propagation ANN model with two inputs and two outputs was created. The Levenberg-Marquardt back-propagation algorithm was selected as a training function, the gradient descent method with momentum weighting and bias learning function as an adaptive learning function, and mean squared normalized error as a performance function. The first layer of the network consists of three neurons, the second layer of six neurons with a tangent sigmoid activation function, and the outputlayer of two outputs with a linear transfer function. The initial values of weights and biases were selected randomly. Input and layer of two outputs with a linear transfer function. The initial values of weights and biases were selected randomly. Input and output data were rescaled to have the minimum possible determination errors. The microprocessor-based method is a low-cost determination system independent of computers and can be used in both laboratory and industrial applications.",
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            "given": "Hakan",
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    "keywords": [
        "artificial neural network, dielectric constant, microcontroller"
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    "translations": [
        {
            "language": "en",
            "title": "Forecasting the Dielectric Properties of the Insulation Material using an Embedded ANN Microcontroller",
            "abstract": "This paper describes a laboratory application for the forecasting the dielectric properties, using an artificial neural network embedded in a microprocessor. The dielectric permittivity and loss factor of the insulation material were determined as a function of temperature and frequency inputs by an implemented ANN model. For this purpose, a three-layer feed-forward back-propagation ANN model with two inputs and two outputs was created. The Levenberg-Marquardt back-propagation algorithm was selected as a training function, the gradient descent method with momentum weighting and bias learning function as an adaptive learning function, and mean squared normalized error as a performance function. The first layer of the network consists of three neurons, the second layer of six neurons with a tangent sigmoid activation function, and the outputlayer of two outputs with a linear transfer function. The initial values of weights and biases were selected randomly. Input and layer of two outputs with a linear transfer function. The initial values of weights and biases were selected randomly. Input and output data were rescaled to have the minimum possible determination errors. The microprocessor-based method is a low-cost determination system independent of computers and can be used in both laboratory and industrial applications.\r\n",
            "keywords": [
                "artificial neural network, dielectric constant, microcontroller"
            ]
        }
    ],
    "subjects": [],
    "subject_names": [],
    "published_date": "2017-12-08",
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    "volume": "1",
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    "page_first": "226",
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        "short_name": "ISMSIT2017",
        "full_title": "International Symposium on Multidisciplinary Studies and Innovative Technologies",
        "name": "International Symposium on Multidisciplinary Studies and Innovative Technologies",
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        "location": "Tokat, Türkiye",
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        "title": "International Symposium on Multidisciplinary Studies and Innovative Technologies",
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