An associative memory emotion recognition circuit based on memristive neural network

A neural network and emotion recognition technology, applied in the field of neural network, can solve the problem of low integration degree, and achieve the effect of improving similarity and shortening the time of re-learning.

Active Publication Date: 2020-11-27
CHINA UNIV OF GEOSCIENCES (WUHAN)
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  • Abstract
  • Description
  • Claims
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AI Technical Summary

Problems solved by technology

[0003] The traditional hardware neural network circuit can only rely on transistors to design the synaptic structure circuit in the neural network, which is subject to Moore's law, and the integration degree of this is too low

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  • An associative memory emotion recognition circuit based on memristive neural network
  • An associative memory emotion recognition circuit based on memristive neural network
  • An associative memory emotion recognition circuit based on memristive neural network

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Embodiment Construction

[0023] In order to have a clearer understanding of the technical features, purposes and effects of the present invention, the specific implementation manners of the present invention will now be described in detail with reference to the accompanying drawings.

[0024]When the human perceptron receives good news, it will feel excited, which is an innate response. Similarly, when it receives bad news, it will feel sad; and when a person's message perceptron receives an unknown At first, it cannot judge whether the news is good or bad, so the news will not cause emotional changes. In a certain period of time, the news always appears together with good news or bad news. Later, when the news is perceived by human beings alone, human beings will feel excited or sad. After a period of forgetting, the signal continues If it appears alone or the signal does not appear for a certain period of time, then the message will lose the ability to affect human emotions. However, even so, when ...

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Abstract

The invention provides an associative memory emotion recognition circuit based on a memristive neural network, the circuit includes an input unit, a logic judgment unit, a synapse unit, a learning speed adjustment unit, an output processing unit and an output unit; the input unit is used for The input neuron in the simulated neural network; the output unit is used to simulate the output neuron in the neural network; the circuit is used to realize an associative memory emotion recognition method based on the memristive neural network; A Networked Associative Memory Emotion Recognition Model to Simulate Human Perceptrons. The beneficial effects of the present invention are: the associative memory emotion recognition circuit based on the memristive neural network has a higher degree of integration, realizes the simulation of the change of human learning speed, better simulates the change of human emotion, and improves the ability of intelligent machines to simulate human beings. Possibilities for thinking and behaving; enhancing the biomimetic capabilities and usefulness of simulated neural networks.

Description

technical field [0001] The invention relates to the field of neural networks, in particular to an associative memory emotion recognition circuit based on a memristive neural network. Background technique [0002] Neural network is widely used in the field of artificial intelligence, and it can be seen in technologies such as pattern recognition, image processing and data mining. Since 2012, the neural network based on software has been developed rapidly and widely used. In fact, compared with the neural network implemented by software, the neural network based on hardware can better realize the high-speed parallel processing of algorithms. , as the amount of data is increasing and the models are becoming more and more complex today, high-speed parallel processing and low-power hardware neural network circuits have great research value and practical significance. [0003] The traditional hardware neural network circuit can only rely on transistors to design the synaptic stru...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06N3/00G06N3/063
CPCG06N3/008G06N3/063
Inventor 王雷敏邹化宇
Owner CHINA UNIV OF GEOSCIENCES (WUHAN)
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