Construction of a Neural Network and its Application on Target Classification

• Published in: IEEE Access
• Main Authors: Qing'e Wu, Changsheng Fan, Hu Chen, Donghua Gu
• Format: Article in Journal/Newspaper
• Language: English
• Published: IEEE 2019
• Subjects: Deep learning; convolutional neural network; deep belief network; hydrological feature recognition; Electrical engineering. Electronics. Nuclear engineering; TK1-9971; Antarctic; Antarc*
• Online Access: https://doi.org/10.1109/ACCESS.2019.2900578; https://doaj.org/article/1af75ac0485a45df97290022a72bd829

With the arrival of big data age, the deep convolutional neural networks (DCNNs) with more hidden layers have a more complex network structure and more powerful ability than traditional machine learning methods for feature learning and feature expression. This paper first proposes a model of the DCNN to discuss the basic structure of model, convolutional feature extraction and learning algorithm of convolutional neural network; then, mainly introduces several aspects, that is, the construction of the typical network structure, the training methods, and the parameter settings of network model to be improved and optimized. Moreover, the network model is applied to the classification and recognition of Antarctic hydrological features and compared with some existing classification methods. The novelty of this paper mainly includes two aspects, i.e., the one is that the design and construction of the structure of deep neural network based on deep learning method are performed, namely, connection, weight, calculator, learning training of network, and other design. The other is classifying hydrological characteristics of Pritz Bay in Antarctica's images by the DCNN. The results show that the correct recognition rate of the model method constructed by this paper is the highest. Finally, some problems in the current research are briefly summarized and discussed, and the new direction in the future development is forecasted.