Improvement of accuracy of well-known convoluational neural networks by efficient hybrid strategy

Convolutional neural networks have existed for many years, but recently they have been developed to a greater depth and width than ever before with the increase in the computing power of graphics processing units. Convolutional neural networks are widely used in a variety of artificial intelligence applications, including in manufacturing, agriculture, and medicine. The use of artificial intelligence in various industrial fields is expected to increase. However, improvements in network training efficiency have not resulted in a reciprocal improvement in computational power for identification applications. This paper proposes several types of neural networks that are based on well-known networks such as AlexNet, GoogleNet, and ResNet, whose characteristics have been captured and implemented in lower layer neural networks. From the experimental results, using these hybrid neural networks can bring improved accuracy, with well optimized computational time costs compared to networks that require a large amount of computation.