Image processing method based on dual-mode memristor bridge synaptic circuit

Abstract

The invention provides an image processing method based on a dual-mode memristor bridge synaptic circuit, and the method comprises the steps: constructing the dual-mode memristor bridge synaptic circuit through linear and nonlinear memristor bridge synaptic circuits and an output memristor bridge synaptic circuit based on positive and negative pulses; performing weight simulation according to the dual-mode memristor bridge synaptic circuit, and constructing a dual-mode memristor bridge neural network through the weight simulation; combining the dual-mode memristor bridge synaptic neural network with the cellular neural network, performing edge extraction on the input image through the combined neural network, and identifying a target element in the input image. The beneficial effects of the invention are that the response time and the weight of the dual-mode memristor bridge synaptic neural network are faster and more accurate than those of the conventional dual-mode memristor bridge synaptic neural network. Therefore, the dual-mode memristor bridge synaptic neural network is expected to solve more complex image processing in real time.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to an image processing method based on a dual-mode memristive bridge synaptic circuit. Background technique [0002] Artificial neural network has always been a research hotspot. Since there are hundreds of millions of neurons and synapses in the human brain, implementing synaptic circuits is important for building brain-like machines. In artificial neural networks, a large number of research results have shown that memristors can be used to simulate artificial synapses. Furthermore, it has great potential due to the properties of memory synaptic circuits. Among them, the most widely studied analog synaptic structure is the memory bridge structure, which has the advantages of simple structure, precise control, and high integration. [0003] But in prior art: [0004] "Jin Xianshu et al. (2011). Pulse-based memristor circuits for synapse weighting. IEEE Transactions on C...

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Problems solved by technology

However, it uses a synaptic bridge circuit consisting of five identical memristors, which cannot be represented by a value of 0 for synaptic weights

[0006] "Chen Jing (2018). Neural network and its application based on memristor electronic synapses. [Master's thesis]. [China]: Southwest University." proposed a method consisting of three identical memristors and two MOS The bridge synapse circuit composed of transistors makes up for the deficiency that the weight of the synapse circuit composed of five identical memristors cannot be zero, but it cannot suppress the nonlinearity of memristors

[0007] For the above defects, there is no solution for the time being

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