Multi-stage nerve network model training method based on genetic algorithm

A neural network model and genetic algorithm technology, applied in the field of data mining, can solve the problem of input parameters affecting output in stages, and achieve the effect of excellent prediction effect

Inactive Publication Date: 2016-02-03
NAT COMP NETWORK & INFORMATION SECURITY MANAGEMENT CENT +1
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Problems solved by technology

[0005] The purpose of the present invention is to overcome the deficiencies of the prior art, provide a multi-stage neural network model training method based on genetic algorithm, and solve the control problem that input parameters affect output in stages in engineering

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  • Multi-stage nerve network model training method based on genetic algorithm
  • Multi-stage nerve network model training method based on genetic algorithm
  • Multi-stage nerve network model training method based on genetic algorithm

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[0037] Embodiments of the present invention are described in further detail below in conjunction with the accompanying drawings:

[0038] A genetic algorithm-based multi-stage neural network model training method is implemented for the multi-stage neural network, and the multi-stage neural network is a neural network structure with multiple input layers. The multi-stage neural network system is mainly used in the case where the input parameters in engineering control have a chronological order, that is, the product processing process is different before and after, and the neural network structure with only one input layer cannot obtain accurate parameter control results. The quality of the neural network model is greatly affected by the initial weight and threshold. The genetic algorithm is used to select a set of reasonable initial weights for the neural network by virtue of its global search characteristics, so as to set the initial weight of the network as locally as possibl...

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Abstract

The invention relates to a multi-stage nerve network model training method based on a genetic algorithm. The multi-stage nerve network model training method is technologically characterized by comprising the steps of preprocessing data for eliminating the physical dimension effect of a field; constructing a multi-input-layer network structure according to the number of nodes in an input layer, the number of nodes in a hidden layer and the number of nodes in an output layer; training an initial weight and a threshold by means of the genetic algorithm; updating the weight by means of an iterative algorithm; determining whether a model termination condition is satisfied according to the number of iteration times and a model error, terminating the model if the model termination condition is satisfied, and otherwise updating the weight again. According to the multi-stage nerve network model training method, a multi-stage nerve network structure is constructed for aiming at a problem that parameters in process control are periodical and deteriorate output. According to a fact that the genetic algorithm has a global searching characteristic, the multi-stage nerve network model training method is used for selecting a group of relatively reasonable initial weight for the network structure, thereby preventing local minimum point in network training, and settling a problem that the nerve network structure with only one input layer cannot settle a problem of product processing speed reduction caused by incapability of settling the parameters in a time sequence in engineering control.

Description

technical field [0001] The invention belongs to the technical field of data mining, and in particular relates to a genetic algorithm-based multi-stage neural network model training method. Background technique [0002] With the rapid development of computer technology, especially the continuous application of Internet technology, people's ability to use network information technology to generate and collect data has been greatly improved, and the data has shown a rapid growth trend. How to obtain the required information from massive data has become an urgent research problem. Faced with such a challenge, Data Mining technology emerges at the historic moment, using data mining technology to obtain hidden useful information from these massive data. However, due to the explosive growth of data, how to use data mining technology to quickly and effectively obtain hidden useful information from massive data is becoming more and more important. Therefore, data mining technology ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06N3/12
Inventor 王洋黄瑞陈训逊苏卫卫吴震田凯蒋旭
Owner NAT COMP NETWORK & INFORMATION SECURITY MANAGEMENT CENT
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