Parallel learning soft measurement modeling method for industrial big data

A modeling method and soft-sensing technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve problems such as difficult industrial applications, large performance differences, and poor convergence

Active Publication Date: 2020-02-18
CHINA UNIV OF MINING & TECH
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Problems solved by technology

However, the traditional feedforward neural network has poor convergence, easy to fall into local optimum (such as backpropagation algorithm), sensitivity to data characteristics leads to large performance differences (such as radial basis function) and excessive human intervention ( Such as random vector function link netw

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  • Parallel learning soft measurement modeling method for industrial big data
  • Parallel learning soft measurement modeling method for industrial big data
  • Parallel learning soft measurement modeling method for industrial big data

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[0064] In order to make the purpose, technical solutions, and advantages of this application clearer, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the application, and not used to limit the application.

[0065] Reference to "embodiments" herein means that a specific feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art clearly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0066] reference figure 1 As shown, figure 1 It i...

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Abstract

The invention discloses a parallel learning soft measurement modeling method for industrial big data. The method comprises the steps of S20, dividing sample data into M training sets, adopting a random configuration network parallel learning strategy combining a point increment algorithm and a block increment algorithm, and synchronously establishing and solving a candidate hidden layer node poolmeeting a supervision mechanism for the M training sets; S30, selecting an optimal candidate node from the candidate hidden layer node pool based on a residual steepest descent principle, and adding the optimal candidate node as a hidden layer growth node to the current network; s40, if the model parameters of the current network reach a stop standard, determining a soft measurement model according to the corresponding model parameters; and S50, if the model parameter of the current network does not reach the stop standard, updating the block number M of the sample data in the next iteration according to the current hidden layer node number, returning to execute the step S20 until the model parameter of the current network reaches the stop standard, and determining the soft measurement model according to the model parameter when the model parameter reaches the stop standard.

Description

Technical field [0001] The invention relates to the technical field of industrial process measurement, in particular to a parallel learning soft-sensing modeling method for industrial big data. Background technique [0002] With the development of intelligent manufacturing technology, the parameters that industrial processes care about gradually extend to operational indicators that reflect product quality. However, these online detectors for operating indicators are expensive and often have a large lag, which makes the adjustments not timely enough, which makes it difficult to guarantee product quality. The soft-sensing modeling method is a technical method for predicting dominant variables with easily measurable auxiliary variables by establishing mathematical models between industrial process variables. In recent years, neural network algorithms have been gradually applied in the field of industrial process soft measurement. However, traditional feedforward neural networks h...

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Application Information

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IPC IPC(8): G06N3/04G06N3/08
CPCG06N3/08G06N3/048G06N3/045
Inventor 代伟李德鹏马磊杨春雨马小平
Owner CHINA UNIV OF MINING & TECH
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