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An intelligent solid waste treatment method based on dynamic deep belief network

A deep belief network and solid waste technology, applied in physical realization, neural learning methods, biological neural network models, etc., can solve problems such as low efficiency, high computing costs, and large errors, so as to reduce waste of resources and overcome design problems. Difficult, easily identifiable effects of

Active Publication Date: 2021-08-27
JIANGNAN UNIV
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Problems solved by technology

In this way, not only the error is large, but also the calculation cost is high and the efficiency is low

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  • An intelligent solid waste treatment method based on dynamic deep belief network
  • An intelligent solid waste treatment method based on dynamic deep belief network
  • An intelligent solid waste treatment method based on dynamic deep belief network

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Embodiment Construction

[0075] The specific embodiments of the present invention will be further described below with reference to the drawings and technical solutions.

[0076] like Figure 4 As shown, a solid waste intelligent processing method based on a dynamic depth confidential network, the specific steps are as follows:

[0077] Step 1, measure the GDP, dangerous substance, solid waste amount, smelting waste slag, furnace coal ash, slag, tail, and data pretreatment, data pretreatment, and data pretreatment data, resulting in training and test data set.

[0078] Since each data of solid waste is often not in the same order, it is necessary to normalize the solid waste data to be between [0, 1], which is conducive to improving the training speed of the network. The normalization formula is:

[0079]

[0080] in, Feature value for solid waste data set, x max X min The maximum and minimum values ​​of all features of the solid waste dataset are respectively, and X is a normalized solid waste data set...

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Abstract

The invention proposes a solid waste intelligent processing method based on a dynamic deep belief network, which belongs to the field of deep learning and solid waste intelligent processing. This method firstly proposes a DDBN using a dynamic increase and decrease branch algorithm, so that the DDBN can increase the hidden layer neurons and hidden layers according to the current training situation during the training process, and remove redundant neurons, effectively optimizing the network structure of the DDBN. Then, taking advantage of DDBN’s ability to effectively extract the main features of the original data, DDBN is used to describe the random, discrete, and non-linear feature vectors of solid waste effectively, making it easier to identify the state features of the time series and ensuring that the original data is not lost. The main information of the data. At the same time, according to the state description of the extracted solid waste, DDBN is used to predict the optimal combustion behavior suitable for its state, which reduces the waste of resources caused by blind combustion behavior and realizes the intelligent treatment of solid waste.

Description

Technical field [0001] The present invention belongs to the field of deep learning, solid waste intelligence treatment, and proposes a dynamic deep belief network (DDBN) model using dynamic increased or decreasing algorithm, which can effectively optimize the network structure of deep confidence networks. To solve the problem of intelligent processing of bulk solid waste in light industry. Background technique [0002] The development of the light industry is currently facing a great environmental pressure and arduous pollution reduction processing task requirements. With the development of the national economy, the market demand for fermentation and papermaking products has greatly increased, although the industry's pollution emissions in the industry have decreased significantly, due to the rapid magnification of production capacity, the total amount of industry solid waste emissions is still increasing. In order to realize the relevant goals of industry energy conservation and...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/66G06N3/06G06N3/08
CPCG06N3/061G06N3/08G06V30/194
Inventor 宋威张士昱王晨妮
Owner JIANGNAN UNIV
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