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作 者:沈广东 SHEN Guangdong(Quanzhou Institute of Information Engineering,Fujian Quanzhou 362000 China)
出 处:《长江信息通信》2024年第12期54-57,共4页Changjiang Information & Communications
摘 要:由于传统的CNN模型在处理一维信号数据时,往往存在性能不足的问题,难以有效地修复计算机网络通信中的错误数据。针对上述问题,提出基于改进卷积神经网络的计算机网络通信错误数据修复方法。首先,基于改进卷积神经网络提取计算机网络通信错误数据特征,通过合理选择和提取特征,可以为后续的错误数据修复提供有力的支持。其次,通过构建改进卷积神经网络模型,在实际应用中,还需要根据具体任务和数据特点进行进一步的调整和优化。最后,将处理后的数据输入到改进后的卷积神经网络中进行训练,实现了通信错误数据的修复。实验结果表明,相比传统方法,基于改进卷积神经网络的计算机网络通信错误数据修复方法在修复精准度方面取得了显著的优势,证明了该方法在提升数据修复准确率和效率方面的有效性。因此,该方法为计算机网络通信中的错误数据修复问题提供了一种有效的解决方案,具有广泛的应用前景。Because the traditional CNN model often has the problem of insufficient performance when processing the one-dimensional signal data,it is difficult to effectively repair the wrong data in the computer network communication.For the above problems,a computer network communication error data repair method based on improved convolutional neural network is proposed.First,the extraction of computer network communication error data features based on improved convolutional neural network,reasonable selection and extraction of features can provide strong support for the subsequent error data repair.Secondly,by constructing the convolutional neural network model,further adjustment and optimization should be made according to specific tasks and data characteristics.Finally,the processed data is input into the improved convolutional neural network for training to realize the repair of communication error data.The experimental results show that,compared with the traditional method,the computer network communication error data repair method based on the improved convolutional neural network has achieved significant advantages in the repair accuracy,which proves the effectiveness of this method in improving the accuracy and efficiency of data repair.Therefore,this method provides an effective solution to the problem of error data repair in computer network communication and has broad application prospects.
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