Board thickness intelligent control method based on active learning

A technology of active learning and intelligent control, applied in non-electric variable control, self-adaptive control, material size control and other directions, can solve the problems of inability to improve the accuracy of the control system, poor system self-adaptation ability, and limited intelligent control level.

Inactive Publication Date: 2016-01-20
NORTH CHINA UNIVERSITY OF SCIENCE AND TECHNOLOGY
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AI Technical Summary

Problems solved by technology

[0004] In view of the above technical problems, the present invention proposes an intelligent control method for plate thickness based on active learning, which is used to overcome the limited intelligent control level of the existing hydraulic automatic thickness control (AGC) system, the poor self-adaptive ability of the system, and the inability to self-learning Ways to improve the accuracy of the control system and other issues

Method used

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  • Board thickness intelligent control method based on active learning
  • Board thickness intelligent control method based on active learning
  • Board thickness intelligent control method based on active learning

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0088] Embodiment 1: training experiment of dynamic neural network

[0089] Such as figure 2 Construct a network model, set two neurons in the input layer of the network, three neurons in the hidden layer, and three neurons in the output layer, that is, the 2-3-3 structure, the learning step size is 0.03, the momentum factor is 0.2, and the activation function Use the S function. In the experiment, 5000 unlabeled samples were selected for training, and the number of initially randomly selected training samples was set to 200, and the active learning algorithm was used to select training samples that meet the requirements from 5000 marked samples and add them to the training sample set. The meta sensitivity threshold is , the maximum number of dynamic network training steps is , the output maximum error . Figure 4 Describes the dynamics of training through three methods: the active learning (MUSAL) algorithm based on the improved uncertainty sampling strategy, the...

Embodiment 2

[0090] Embodiment 2: the experiment of the intelligent control developmental model work based on active learning

[0091] Set the desired rigid thickness , requiring the maximum deviation allowed . The simulation curve of actual plate thickness output and plate thickness error changing with rolling time Such as Figure 5 and Figure 6 shown. The control method of the present invention can make the actual output thickness of the system reach the expected value , output at It starts to be stable when it is around , and can finally remain stable; the output error meets the requirements, at Within the range, the overshoot is very small; while the output of the passive learning dynamic network controller has finally reached a balance, but there is a slight error between the expected value, and it takes a long time to reach stability, and the overshoot is large. In this way, the high efficiency and applicability of the present invention are verified.

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Abstract

The invention relates to a board thickness intelligent control method based on active learning, which belongs to the field of intelligent control technology. Self-learnable performance of a nerve network is used as a theoretical basis. A dynamic nerve network is combined with active learning; the parameter of a PID controller is adjusted in an online manner; and a development model based on active learning is constructed, thereby establishing an intelligent control system for thickness of band steel, so that the board thickness control system can perform self adjustment at proper time, and the control performance of the board thickness control system is improved through continuous training of the dynamic nerve network. The board thickness intelligent control method has functions of providing a mathematical model with high generalization capability and wide application range for online control parameter adjustment of the system; combining active learning with the dynamic nerve network, and improving self-learning capability of the network through active learning and acquiring network training samples, thereby improving adaptive capability of the system and realizing intelligent in a real meaning.

Description

technical field [0001] The invention relates to an intelligent control method for plate thickness based on active learning, which belongs to the technical field of intelligent control. Background technique [0002] The iron and steel industry occupies an important position in my country's national economy and is an important basic industry. With the advancement of economic globalization and today's increasingly scarce energy, it has become a common pursuit of the steel industry to increase technological innovation, improve production technology, and improve product quality. The neural network in the intelligent technology is just one of the successful technologies to solve the control precision of the Automatic Gauge Control (AGC) system and the uncertain factors in the rolling process. With the development of computer technology, people use the method of artificial intelligence to store the operator's adjustment experience as knowledge in the computer. According to the act...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B13/04G05D5/02
Inventor 史涛任红格李冬梅李福进向迎帆霍美杰徐少彬
Owner NORTH CHINA UNIVERSITY OF SCIENCE AND TECHNOLOGY
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