Method and system for adaptively setting deep belief network (DBN) parameter

A deep belief network and fitness function technology, applied in the field of data processing, can solve problems such as difficulty in obtaining network parameters, adaptive setting parameters, and inability to obtain recognition effects, so as to achieve the effect of improving accuracy and good recognition results

Pending Publication Date: 2017-06-27
ZHENGZHOU YUNHAI INFORMATION TECH CO LTD
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Problems solved by technology

In this way, since the parameters cannot be adaptively set according to the sample, it is difficult to o

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  • Method and system for adaptively setting deep belief network (DBN) parameter
  • Method and system for adaptively setting deep belief network (DBN) parameter
  • Method and system for adaptively setting deep belief network (DBN) parameter

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[0048] The core of the present invention is to provide a method and system for adaptively setting parameters of a deep confidence network based on a genetic algorithm. The parameters of the deep confidence network are adaptively adjusted through the genetic algorithm to obtain a better recognition effect.

[0049] In order to make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be described clearly and completely in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of the embodiments of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

[0050] The following explains...

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Abstract

The invention discloses a method for adaptively setting a deep belief network (DBN) parameter based on a genetic algorithm. The method comprises the steps of setting an initial value for a DBN parameter to be optimized, and establishing a fitness function for setting the DBN parameter according to the initial value; obtaining optimal individual data by a genetic algorithm according to the fitness function; and anti-coding the optimal individual data to obtain an optimal DBN parameter. The problems that a manual parameter setting method is inefficient and often cannot achieve the optimal parameter setting are solved. The genetic algorithm can be used for improving the accuracy, the initial parameter of the DBN can be automatically determined according to input samples, and then the optimal network topology is obtained. The optimized parameter can be used for accurately learning the advanced characteristics of the sample data, so that the DBN can obtain better recognition results. The invention also discloses a system for adaptively setting the DBN parameter based on the genetic algorithm, which has the above-mentioned advantageous effects.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a method and system for adaptively setting parameters of a deep belief network. Background technique [0002] Currently, Deep Learning is a hot research topic in the field of object recognition. The concept of deep learning was proposed by Hinton et al. in 2006. It mainly simulates the learning process of the human brain through the neural network (Neural Network). neurons, and achieve the purpose of reconstructing objects through node weight training. Its most notable feature is that the feature extraction process reduces human intervention as little as possible. Deep learning is essentially a greedy algorithm, which is a neural network-like structure, but generally speaking, the network layer can have multiple layers, unlike artificial neural networks, which only have three layers. At the bottom layer of the deep learning model, that is, the input layer of the sample,...

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Application Information

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IPC IPC(8): G06K9/62G06N3/12
CPCG06N3/126G06F18/217G06F18/214
Inventor 时帅兵陈东河
Owner ZHENGZHOU YUNHAI INFORMATION TECH CO LTD
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