Abstract
Complex network science is an interdisciplinary field of study based on graph theory, statistical mechanics, and data science. With the powerful tools now available in complex network theory for the study of network topology, it is obvious that complex network topology models can be applied to enhance artificial neural network models. In this paper, we provide an overview of the most important works published within the past 10 years on the topic of complex network theory-based optimization methods. This review of the most up-to-date optimized neural network systems reveals that the fusion of complex and neural networks improves both accuracy and robustness. By setting out our review findings here, we seek to promote a better understanding of basic concepts and offer a deeper insight into the various research efforts that have led to the use of complex network theory in the optimized neural networks of today.
Funder
Korea Institute of Energy Technology Evaluation and Planning
Subject
General Mathematics,Engineering (miscellaneous),Computer Science (miscellaneous)
Reference41 articles.
1. Rosenblatt, F. (1961). Principles of Neurodynamics. Perceptrons and the Theory of Brain Mechanisms, Cornell Aeronautical Lab Inc.
2. Methods for interpreting and understanding deep neural networks;Montavon;Signal Process.,2018
3. Recent advances in convolutional neural networks;Gu;Pattern Recognit.,2018
4. (2022, November 01). Available online: https://learnopencv.com/number-of-parameters-and-tensor-sizes-in-convolutional-neural-network/.
5. Efficient Hopfield pattern recognition on a scale-free neural network;Stauffer;Eur. Phys. J. B-Condens. Matter Complex Syst.,2003
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献