Application-oriented usage of neural networks
DOI: 10.31673/2412-4338.2019.024452
Abstract
Article dwells upon most recent trends emerging in the field of artificial neural network, such as Capsule Networks, Convolutional Neural Networks (CNN), Deep Reinforcement Learning (DRL), Lean and Augmented Learning, Supervised Model, Networks With Memory Model, Hybrid Learning Models. Comparative analysis of usage different types of neural networks architecture is provided.
Keywords: neural networks, trends in neural networks, artificial neural networks, artificial neurons, deep learning for neural networks.
References
1. T. Sun, H. Pei, Y. Pan, and C. Zhang, “Robust adaptive neural network control for environmental boundary tracking by mobile robots,” International Journal of Robust and Nonlinear Control 2 (2013), Vol. 23, 123–136.
2. W. Sun, Z. Zhao and H. Gao, “Saturated adaptive robust control for active suspension systems,” IEEE Transactions on Industrial Electronics 9 (2013), Vol. 60, 3889–3896.
3. F. F. El-Sousy, “Adaptive hybrid control system using a recurrent rbfn-based self-evolving fuzzy-neural-network for pmsm servo drives,” Applied Soft Computing (2014), Vol. 21, 509–532.
4. M. M. Ferdaus, S. G. Anavatti, M. A. Garratt and M. Pratama, “Fuzzy clustering based nonlinear system identification and controller development of pixhawk based quadcopter,” in Advanced Computational Intelligence (ICACI), 2017 Ninth International Conference on. IEEE, (2017), 223–230.
5. F. Lv, G. Yang, W. Yang, X. Zhang and K. Li, “The convergence and termination criterion of quantum-inspired evolutionary neural networks”, Neurocomputing (2017), Vol. 238, 157–167.
6. H. V. H. Ayala and L. dos Santos Coelho, “Cascaded evolutionary algorithm for nonlinear system identification based on correlation functions and radial basis functions neural networks,” Mechanical Systems and Signal Processing (2016), Vol. 68, 378–393.
7. I. Vlachos, T. Deniz, A. Aertsen and A. Kumar, “Recovery of dynamics and function in spiking neural networks with closed-loop control,” PLoS computational biology 2 (2016), Vol. 12, e1004720.
8. A.J. Al-Mahasneh, S.G.Anavatti and M.A.Garratt, “The Development of Neural Networks applications from Perceptron to Deep Learning”, Conference paper for School of Engineering and Information Technology, The University of New South Wales at the Australian Defense Force Academey Canberra, ACT 2612 Australia (2017), 118-121.
9. R. Mukhometzianov, J. Carrillo, “CapsNet comparative performance evaluation for image, Classification”, University of Waterloo, ON, Canada (2017).
10. P.M. Radiuk, “Impact of Training Set Batch Size on the Performance of Convolutional Neural Networks for Diverse Datasets”, Riga Technical University, Information Technology and Management Science, December (2017), Vol. 20, 20–24.