Associative memory emotion recognition circuit based on memristor neural network

Abstract

The invention provides an associative memory emotion recognition circuit based on a memristor neural network. The circuit comprises an input unit, a logic judgment unit, a synaptic unit, a learning speed regulation unit, an output processing unit and an output unit. The input unit is used for simulating an input neuron in a neural network. The output unit is used for simulating output neurons in the neural network. The circuit is used for realizing an associative memory emotion recognition method based on a memristor neural network. an associative memory emotion recognition model is established based on the memristor neural network by using the neural network to simulate the human perceptron. The associative memory emotion recognition circuit based on the memristor neural network has the beneficial effects that the integration degree of the associative memory emotion recognition circuit based on the memristor neural network is higher, simulation of human learning speed changes is achieved, the human emotion changes are better simulated, and the probability that an intelligent machine simulates human thinking and behaviors is improved, and the bionic capability and the practicability of the simulated neural network are enhanced.

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...

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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