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The Application of RBF Neural Network Model Based on Deep Learning for Flower Pattern Design in Art Teaching

机译:基于深度学习的RBF神经网络模型在艺术教学中花纹设计的应用

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摘要

The rapid growth of artificial intelligence technology has been deployed in art teaching and learning. Radial basis function (RBF) networks have a completely different design compared to most neural network architectures. Most neural networks consist of multiple layers that can introduce nonlinearity by repetitive application of nonlinear activation functions. In this research, people will study the application of the RBF neural network model based on deep learning in flower pattern design in art teaching. The image classification process is finding and labeling groups of pixels or vectors inside an image based on rules. Deep learning is a type of machine learning that uses artificial neural networks to replicate the structure and function of the human brain. The proposed model uses the RBF neural network-based deep learning model in flower pattern design in art teaching and provides efficient results.
机译:人工智能技术的快速发展已部署在艺术教学中。与大多数神经网络架构相比,径向基函数 (RBF) 网络具有完全不同的设计。大多数神经网络由多层组成,这些层可以通过重复应用非线性激活函数来引入非线性。在这项研究中,人们将研究基于深度学习的RBF神经网络模型在艺术教学中花纹设计中的应用。图像分类过程是根据规则查找和标记图像中的像素或矢量组。深度学习是一种机器学习,它使用人工神经网络来复制人脑的结构和功能。该模型在艺术教学中的花纹设计中使用了基于RBF神经网络的深度学习模型,并提供了高效的结果。

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