Handwritten digit identification method based on improved spiking neuron networks

Abstract

The invention discloses an unsupervised learning method of handwritten digit identification. The method comprises the following steps: firstly, establishing a spiking neural network model, and determining the connection mode between neurons according to an analog biological information processing mechanism; secondly, establishing a spiking time dependent plasticity model according to the synaptictrace learning rules; and finally, optimizing the pre-synaptic initial weight and the synaptic delay in the spiking neural networks by using a genetic algorithm, training and testing the MNIST (MixedNational Institute of Standards and Technology database) data set, thus achieving the unsupervised learning of handwritten digit identification. According to the unsupervised learning method of handwritten digit identification, experiments show that the training speed is effectively improved and the recognition accuracy is improved.

Description

technical field [0001] The invention relates to the technical field of image recognition, in particular to a handwritten digit recognition method based on an improved impulse neural network. Background technique [0002] Digital is the main medium for people to communicate and communicate, and it is the information carrier for the development of the world economy. For a long time, the processing method of manually identifying numbers and entering them has problems such as heavy workload, high cost, low efficiency, and poor timeliness. We urgently need to use the "reading" method to liberate people from heavy manual labor. Among them, the use of digital identification technology to realize automatic entry of information will undoubtedly become the fundamental method to solve this key problem. [0003] Handwritten digit recognition is an important branch of image recognition, which integrates artificial intelligence, formal language, statistical decision-making, information t...

AI Technical Summary

Problems solved by technology

The support vector machine (Support Vector Machine, SVM) based on the statistical model has greatly improved the accuracy of handwritten digit recognition, but it has too many training parameters and complex calculations, which increases the recognition process time

Convolutional neural networks (CNNs), as a current research hotspot in the field of image recognition, have a very high recognition rate, but overfitting occurs during the recognition training process, and in practice, there are few labeled data , there are many data without labels, and it is very time-consuming and boring to manually set labels for each data. At the same time, convolutional neural networks need to set and adjust a large number of parameters, which are important reasons for restricting convolutional neural networks.

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