Semi-supervised neural network model and soft-sensing modeling method based on model

A neural network model and neural network technology, applied in the field of industrial process prediction and control, can solve the problems of serious process nonlinearity and many unlabeled samples

Inactive Publication Date: 2017-12-22
ZHEJIANG UNIV
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Problems solved by technology

[0005] Aiming at the problems of few labeled samples, many unlabeled samples and serious process nonlinearity in the current industrial process, the present invention proposes a soft sensor modeling method based on semi-supervised neural netw...

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  • Semi-supervised neural network model and soft-sensing modeling method based on model

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[0022] The present invention will be further described in detail below in combination with specific embodiments.

[0023] A kind of semi-supervised neural network model, described model is made up of autoencoder and neural network, is divided into three layers, the first layer is input layer, the second layer is hidden layer, the third layer is output layer, autoencoder The input layer and hidden layer are shared with the neural network model, and the output layer is divided into the autoencoder output layer and the neural network model output layer. The input variable of the input layer is x, and the weight and bias from the input layer to the hidden layer are ω 1 and b 1 , the weights and biases from the hidden layer to the output layer of the neural network are ω y and b y , the weights and biases from the hidden layer to the output layer of the autoencoder are ω 2 and b 2 , the reconstruction value of the autoencoder output layer output is The predicted value of the ...

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Abstract

The present invention discloses a semi-supervised neural network model and a soft-sensing modeling method based on the model. The model is divided into three layers, wherein the first layer is an input layer, the second layer is a hidden layer, and the third layer is an output layer, wherein the output layer is divided into an auto-encoder output layer and a neural network model output layer; and a self-encoder and a neural network model share the input layer and the hidden layer. The modeling method is composed of the self-encoder and the neural network and can effectively solve the problem of inaccurate soft sensing modeling caused by the scarcity of labeled samples and abundance of unlabeled samples so as to build a more accurate semi-supervised soft-sensing model and realize process monitoring and corresponding control.

Description

technical field [0001] The invention belongs to the field of industrial process prediction and control, and relates to a semi-supervised neural network model and a soft sensor modeling method based on the model. Background technique [0002] In the actual industrial production process, there are often more or less key process variables that cannot be detected online. In order to solve this problem, a variable that is easier to detect in the collection process is constructed according to an optimal standard. With these variables as input and key process variables as output mathematical model, the online estimation of key process variables is realized. This is the soft sensor modeling commonly used in industrial processes. [0003] The development of statistical process soft-sensing modeling has an extremely significant demand for large-scale industrial data. However, there are still many problems in soft sensor modeling. The complexity of the system in the industrial proces...

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Application Information

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IPC IPC(8): G05B13/04G06N3/04
CPCG05B13/042G06N3/045
Inventor 葛志强李浩宋执环
Owner ZHEJIANG UNIV
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