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首页> 外文期刊>Mathematical Problems in Engineering >A New Generalized Deep Learning Framework Combining Sparse Autoencoder and Taguchi Method for Novel Data Classification and Processing
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A New Generalized Deep Learning Framework Combining Sparse Autoencoder and Taguchi Method for Novel Data Classification and Processing

机译：结合稀疏自动编码器和Taguchi方法的新型通用深度学习框架，用于新型数据分类和处理

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摘要

Deep autoencoder neural networks have been widely used in several image classification and recognition problems, including hand-writing recognition, medical imaging, and face recognition. The overall performance of deep autoencoder neural networks mainly depends on the number of parameters used, structure of neural networks, and the compatibility of the transfer functions. However, an inappropriate structure design can cause a reduction in the performance of deep autoencoder neural networks. A novel framework, which primarily integrates the Taguchi Method to a deep autoencoder based system without considering to modify the overall structure of the network, is presented. Several experiments are performed using various data sets from different fields, i.e., network security and medicine. The results show that the proposed method is more robust than some of the well-known methods in the literature as most of the time our method performed better. Therefore, the results are quite encouraging and verified the overall performance of the proposed framework.
机译：深度自动编码器神经网络已广泛用于几个图像分类和识别问题，包括手写识别，医学成像和面部识别。深度自动编码器神经网络的整体性能主要取决于所用参数的数量，神经网络的结构以及传递函数的兼容性。但是，不合适的结构设计会导致深度自动编码器神经网络的性能下降。提出了一种新颖的框架，该框架主要将Taguchi方法集成到基于深度自动编码器的系统中，而无需考虑修改网络的整体结构。使用来自不同领域（即网络安全和医学）的各种数据集进行了几次实验。结果表明，所提出的方法比文献中的某些众所周知的方法更健壮，因为大多数时候我们的方法表现更好。因此，结果令人鼓舞，并验证了拟议框架的整体性能。

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