  Recently, Machine Learning (ML), Artificial Intelligence (AI), and
Convolutional Neural Network (CNN) have made huge progress with broad
applications, where their systems have deep learning structures and a large
number of hyperparameters that determine the quality and performance of the
CNNs and AI systems. These systems may have multi-objective ML and AI
performance needs. There is a key requirement to find the optimal
hyperparameters and structures for multi-objective robust optimal CNN systems.
This paper proposes a generalized Taguchi approach to effectively determine the
optimal hyperparameters and structure for the multi-objective robust optimal
CNN systems via their objective performance vector norm. The proposed approach
and methods are applied to a CNN classification system with the original ResNet
for CIFAR-10 dataset as a demonstration and validation, which shows the
proposed methods are highly effective to achieve an optimal accuracy rate of
the original ResNet on CIFAR-10.
