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دسته بندی:
یادگیری تقویتی - Reinforcement-Learning
سال انتشار:
2020
عنوان انگلیسی مقاله:
Reinforcement learning based optimizer for improvement of predicting tunneling-induced ground responses
ترجمه فارسی عنوان مقاله:
بهینه ساز مبتنی بر یادگیری تقویتی برای بهبود پیش بینی پاسخ های ناشی از tunneling
منبع:
Sciencedirect - Elsevier - Advanced Engineering Informatics, 45 (2020) 101097: doi:10:1016/j:aei:2020:101097
نویسنده:
Pin Zhanga, Heng Lib, Q.P. Hac, Zhen-Yu Yina, Ren-Peng Chend,e,⁎
چکیده انگلیسی:
Prediction of ground responses is important for improving performance of tunneling. This study proposes a novel
reinforcement learning (RL) based optimizer with the integration of deep-Q network (DQN) and particle swarm
optimization (PSO). Such optimizer is used to improve the extreme learning machine (ELM) based tunnelinginduced
settlement prediction model. Herein, DQN-PSO optimizer is used to optimize the weights and biases of
ELM. Based on the prescribed states, actions, rewards, rules and objective functions, DQN-PSO optimizer
evaluates the rewards of actions at each step, thereby guides particles which action should be conducted and
when should take this action. Such hybrid model is applied in a practical tunnel project. Regarding the search of
global best weights and biases of ELM, the results indicate the DQN-PSO optimizer obviously outperforms
conventional metaheuristic optimization algorithms with higher accuracy and lower computational cost.
Meanwhile, this model can identify relationships among influential factors and ground responses through selfpracticing.
The ultimate model can be expressed with an explicit formulation and used to predict tunnelinginduced
ground response in real time, facilitating its application in engineering practice.
Keywords: Tunnel | Ground response | Reinforcement learning | Extreme learning machine | Optimization
قیمت: رایگان
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