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.
CN110110840BActive Publication Date: 2020-11-27CHINA UNIV OF GEOSCIENCES (WUHAN)
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Patent Information

Authority / Receiving Office
CN ยท China
Patent Type
Patents(China)
Current Assignee / Owner
CHINA UNIV OF GEOSCIENCES (WUHAN)
Publication Date
2020-11-27

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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.
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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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