Henan Vocational College of Water Conservancy and Environment, Zhengzhou 450008, Henan Province, China.
Zhengzhou University of Industry Technology, School of Art and Design, Zhengzhou 451100, Henan Province, China.
Comput Intell Neurosci. 2021 Nov 16;2021:3302617. doi: 10.1155/2021/3302617. eCollection 2021.
With the continuous application of the art industry in various fields, more and more people choose to systematically learn the knowledge of the art industry. In the art major, image painting is one of the important contents of the art major. How to improve students' aesthetic quality and comprehensive professional quality is studied, in which the content learning of image painting art is the key. Therefore, we have carried out technical exploration and result analysis based on Gaussian mutation genetic algorithm to optimize the application of neural network in image painting art teaching. We use Gaussian mutation genetic algorithm to study the neural network optimized teaching cloud platform technology. Compared with the traditional algorithm, the algorithm proposed in this paper has more funny computational efficiency, being able to comprehensively evaluate and improve students' aesthetic quality and comprehensive professional quality. Gaussian mutation genetic algorithm can effectively improve the knowledge search ability of the platform and the running speed of the teaching platform. In the future research in the field of art industry, neural network will optimize the teaching cloud platform technology, which has laid a solid foundation for improving students' aesthetic quality and comprehensive professional quality.
随着艺术行业在各个领域的不断应用，越来越多的人选择系统地学习艺术行业的知识。在艺术专业中，意象画是艺术专业的重要内容之一。如何提高学生的审美素质和综合专业素质是研究的重点，意象画艺术的内容学习是关键。因此，我们基于高斯变异遗传算法进行了技术探索和结果分析，以优化神经网络在意象画艺术教学中的应用。我们使用高斯变异遗传算法研究神经网络优化教学云平台技术。与传统算法相比，本文提出的算法具有更高的计算效率，可以全面评估和提高学生的审美素质和综合专业素质。高斯变异遗传算法可以有效地提高平台的知识搜索能力和教学平台的运行速度。在未来的艺术行业研究中，神经网络将优化教学云平台技术，为提高学生的审美素质和综合专业素质奠定坚实的基础。
