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

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...

AI Technical Summary

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 network, which combines autoencoder and neural network model Combined with semi-supervised soft sensor modeling for industrial processes, accurate online estimation of key process variables is realized. The specific technical solutions are as follows:

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