| 1 |
A Smooth Double Proximal Primal-Dual Algorithm for a Class of Distributed Nonsmooth Optimization Problems
Wei Y, Fang H, Zeng XL, Chen J, Pardalos P
IEEE Transactions on Automatic Control, 65(4), 1800, 2020 |
| 2 |
Push-Sum on Random Graphs: Almost Sure Convergence and Convergence Rate
Rezaienia P, Gharesifard B, Linder T, Touri B
IEEE Transactions on Automatic Control, 65(3), 1295, 2020 |
| 3 |
Stochastic learning in multi-agent optimization: Communication and payoff-based approaches
Tatarenko T
Automatica, 99, 1, 2019 |
| 4 |
On convergence rates of game theoretic reinforcement learning algorithms
Hu ZS, Zhu MH, Chen P, Liu P
Automatica, 104, 90, 2019 |
| 5 |
Hydrogen-based self-sustaining integrated renewable electricity network (HySIREN) using a supply-demand forecasting model and deep-learning algorithms
Hwangbo S, Nam K, Heo S, Yoo C
Energy Conversion and Management, 185, 353, 2019 |
| 6 |
Hydrogen production via biomass gasification, and modeling by supervised machine learning algorithms
Ozbas EE, Aksu D, Ongen A, Aydin MA, Ozcan HK
International Journal of Hydrogen Energy, 44(32), 17260, 2019 |
| 7 |
Application of machine learning algorithms in quality assurance of fermentation process of black tea- based on electrical properties
Zhu HK, Liu F, Ye Y, Chen L, Li JY, Gu AH, Zhang JQ, Dong CW
Journal of Food Engineering, 263, 165, 2019 |
| 8 |
Tomato volume and mass estimation using computer vision and machine learning algorithms: Cherry tomato model
Nyalala I, Okinda C, Nyalala L, Makange N, Chao Q, Chao L, Yousaf K, Chen KJ
Journal of Food Engineering, 263, 288, 2019 |
| 9 |
Simulation-based optimization of Markov decision processes: An empirical process theory approach
Jain R, Varaiya P
Automatica, 46(8), 1297, 2010 |
| 10 |
Natural actor-critic algorithms
Bhatnagar S, Sutton RS, Ghavamzadeh M, Lee M
Automatica, 45(11), 2471, 2009 |
