Information Technology Center, Wuxi Vocational Institute of Arts & Technology, Wuxi 214200, China.
Comput Intell Neurosci. 2022 May 25;2022:6213718. doi: 10.1155/2022/6213718. eCollection 2022.
In web design, we need to make full use of computer multimedia technology, which can effectively improve the quality of web design and provide greater convenience. The neural network needs to calculate a large amount of training data in image optimization, and the calculation speed cannot keep up with real-time data processing, resulting in the problem of poor quality of optimized images. This paper analyzes the problems existing in the traditional optimized BP neural network algorithm and puts forward an optimized BP neural network image optimization method which combines the increase of momentum term with the adaptive adjustment of learning rate. This method can speed up the iteration speed and jump out of the situation of premature local minimum. The test results show that the user satisfaction of the text visual effect of the web interface optimized by this method is more than 98% and the user satisfaction of other methods is only about 90%. The visual satisfaction of the web interface optimized by this method is significantly higher than that of the comparative method. The web interface visual optimization effect of the method in this article is better, and it can meet the satisfaction requirements of most users.
在网页设计中,我们需要充分利用计算机多媒体技术,这可以有效地提高网页设计的质量,提供更大的便利。神经网络在图像优化中需要计算大量的训练数据,计算速度跟不上实时数据处理,导致优化图像的质量较差。本文分析了传统优化 BP 神经网络算法中存在的问题,提出了一种结合动量项增加和学习率自适应调整的优化 BP 神经网络图像优化方法。该方法可以加快迭代速度,跳出局部最小值的情况。测试结果表明,采用该方法优化后的网页界面文本视觉效果的用户满意度超过 98%,而其他方法的用户满意度仅约为 90%。采用该方法优化后的网页界面的视觉满意度明显高于对比方法。本文方法的网页界面视觉优化效果更好,可以满足大多数用户的满意度要求。