基于BP神经网络的手写数字识别及优化方法

Handwritten Digit Recognition and Optimization Method Based on BP Neural Network

随着人工智能的发展,数字识别技术也得到了关注并通过各种算法提高了识别准确率。数字识别在安防、交通、邮政等领域发挥越来越重要的作用,是智能城市不可或缺的一环。通过采用包含隐含层的BP神经网络对数字识别进行仿真。首先介绍Mnist数据集、人工神经元模型、激活函数、BP算法等相关概念,详细描述了BP神经网络的原理,并通过实例进行BP网络设计。同时提出了6种优化方式,分别是初始化权值、设置Dropout、选取不同的激活函数、选取不同的代价函数、采用不同优化器、设置学习率。结果表明BP网络在数字识别方面具有实际应用价值,并能通过各种优化方式提高识别精度。

With the development of artificial intelligence, digital recognition technology has also attracted attention and improved the recognition accuracy through various algorithms.Digital identification plays an increasingly important role in security,transportation,postal services and other fields. It is an important link in smart cities. BP neural network with hidden layer was used to simulate digital recognition.Firstly, the related concepts of Mnist data set,artificial neuron model,activation function and BP algorithm were introduced,and the principle of BP neural network was described in detail.At the same time,six optimization methods were proposed,including initializing weights,setting rotation values,selecting different activation functions,selecting different cost functions,using different optimizer and setting learning rate.It is proved that BP network has practical application value in digital identification and can improve the identification precision by various optimization methods.