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Sensitive long-term and short-term memory method based on output variation differentiation

A technology of long-term and short-term memory and output changes, applied in neural learning methods, neural architectures, biological neural network models, etc. Effect

Active Publication Date: 2019-11-19
NANJING UNIV OF INFORMATION SCI & TECH
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Problems solved by technology

[0003] The existing long-term short-term memory network still has a major problem, that is, it uses long-term short-term memory to improve the analysis ability of information in the long-term sequence of the entire video, but there is no response to short-term information at all. ability, which makes the existing long-short-term memory network can only be used for post-event analysis, and cannot achieve good real-time performance and recognition of micro-movements and other content

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  • Sensitive long-term and short-term memory method based on output variation differentiation

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Embodiment Construction

[0033] Below in conjunction with accompanying drawing, technical scheme of the present invention is described in further detail:

[0034] The principle of the present invention is: the core of the LSTM neural network is to add a memory module to learn and extract the relevant information and rules in the middle of the current information, so as to transmit information. A neural unit of the LSTM neural network contains three structures: input gate i t , the forget gate f t and output gate o t , each step size t and its corresponding input sequence is X={x 1 , x 2 ,...,x t}. In order to improve its response ability to short-time information, the present invention adds an input differential sequence similar to the output differential effect

[0035] The present invention is a neural unit of the long-short-term memory network with increased information sensitivity. The status information of the previous node is sent from the input terminal c t-1 Input, whenever data ente...

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Abstract

The invention discloses a sensitive long-term and short-term memory method based on output variation differentiation. The objective of the invention is to improve the response capability of a traditional LSTM neural network to short-time information. According to the method, the neural unit of the long-term and short-term memory network with increased information sensitivity is added, so that theresponse capability of the network to short-term information can be well improved. The application real-time performance of the network is improved. More perfect real-time analysis can be carried out.Micro-actions and other contents can be further analyzed. The application value is improved.

Description

technical field [0001] The invention relates to the field of long-short-term memory network, in particular to a sensitive long-short-term memory method based on output variation differentiation. Background technique [0002] Artificial intelligence is one of the three important disciplines in the 21st century and an important support for national science, economy, and people's livelihood. Among them, the long-short-term memory network (LSTM) is an important algorithm for memory-based recognition, which has been recognized in many aspects including semantics, actions, texts, etc., and has very good value. [0003] The existing long-term short-term memory network still has a major problem, that is, it uses long-term short-term memory to improve the analysis ability of information in the long-term sequence of the entire video, but there is no response to short-term information at all. ability, which makes the existing long-short-term memory network can only be used for post-ev...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04G06N3/08
CPCG06N3/049G06N3/08G06N3/045
Inventor 胡凯郑翡张彦雯卢飞宇
Owner NANJING UNIV OF INFORMATION SCI & TECH
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