Volume tense data modeling method based on determined learning theory

A technology to determine learning theory and temporal data, applied in neural learning methods, biological neural network models, etc., can solve problems such as complex algorithms

Inactive Publication Date: 2013-04-24
SOUTH CHINA UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

Its shortcoming is that in the process of designing the dynamic RBF neural network learning law, it is necessary to construc

Method used

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  • Volume tense data modeling method based on determined learning theory
  • Volume tense data modeling method based on determined learning theory
  • Volume tense data modeling method based on determined learning theory

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Embodiment

[0048] There are many forms of time representation and recording methods involved in massive temporal data. Here, a massive temporal data generated by a discrete nonlinear dynamic system Henon system is considered. ,

[0049] x 1 ( k + 1 ) = x 2 ( k ) + 1 - 1.4 x 1 2 ( k ) + d 1 ( k )

[0050] x 2 (k+1)=0.3x 1 (k)

[0051] where, assuming x 1 The subsystem of is unknown, d 1 (k) is white noise interference, some main parameters in the present embodiment: system state initial value is x 1 (1)=0,x 2 (1)=-0.2.

[0052] The steps of adopting the m...

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Abstract

The invention discloses a volume tense data modeling method based on a determined learning theory. The method aims at volume tense data with noise, wherein the volume tense data are ubiquitous in science researches and engineering practices and generated a dispersed nonlinear system normally. A modeling process comprises that the volume tense data are defined and preprocessed through filtering; a neural network identifier is structured by adopting a dynamic radial basis function (RBF) neural network, and a reasonable adjusting rule of RBF neural network weight is designed; and a model is built for the neural network of the volume tense data. The volume tense data modeling method has the advantages of simplifying modeling of the volume tense data and performance analysis, being applied to rapid similarity judgment of the volume tense data and the like.

Description

technical field [0001] The invention belongs to the problem of massive temporal data modeling and learning, and in particular relates to a modeling method of massive temporal data based on deterministic learning theory. Background technique [0002] With the rapid development of information science and technology, especially information collection technology and the Internet, a large amount of time-varying data is generated and stored every day in many fields such as scientific research, engineering manufacturing, and social economy. Most of these data can be regarded as generated by multidimensional nonlinear dynamic systems and have time dependence, so they can be called massive temporal data, temporal sequence or time series. With the passage of time, the data volume of massive temporal data will increase rapidly and develop into massive data (or big data). It is of great significance to study the modeling method of massive temporal data for mining the valuable informati...

Claims

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Application Information

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IPC IPC(8): G06N3/08
Inventor 王聪袁成志胡俊敏
Owner SOUTH CHINA UNIV OF TECH
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