In this study,we implement forward modeling and inversion based on deep-learning strategies using an optimal nearly analytic discrete(ONAD)method.The forward-modeling method combines the ONAD method with recurrent neu...In this study,we implement forward modeling and inversion based on deep-learning strategies using an optimal nearly analytic discrete(ONAD)method.The forward-modeling method combines the ONAD method with recurrent neural network(RNN)for the fi rst time.RNN is a type of neural network that is suitable for sequential data,which uses information from both previous and current times to obtain output information.We express the ONAD method using an RNN framework to advance the time iteration of an acoustic equation.This process can simplify programming using RNN and convolution kernels.Next,we use deep learning based on the proposed forward-modeling method to study full waveform-inversion problems.Because the main purpose of inversion is to minimize the error between real and synthetic data,inversion is essentially an optimization problem.Many new optimizers are available in the framework of deep learning,such as the Adam and Nadam optimizers,which are used for optimizing velocity model in the inversion process.We perform six numerical experiments.The first two experiments demonstrate the forward-modeling results,which indicate that the forward-modeling method can effectively suppress numerical dispersion and improve computational effi ciency.The other four experiments demonstrate the inversion results,which show that the method proposed in this paper can eff ectively realize inversion imaging.We compare several optimizers used in deep learning and find that the Nadam optimizer has faster convergence and better effectiveness based on the ONAD method combined with RNN.展开更多
In this paper, we report in-depth analysis and research on the optimizing computer network structure based on genetic algorithm and modified convex optimization theory. Machine learning method has been widely used in ...In this paper, we report in-depth analysis and research on the optimizing computer network structure based on genetic algorithm and modified convex optimization theory. Machine learning method has been widely used in the background and one of its core problems is to solve the optimization problem. Unlike traditional batch algorithm, stochastic gradient descent algorithm in each iteration calculation, the optimization of a single sample point only losses could greatly reduce the memory overhead. The experiment illustrates the feasibility of our proposed approach.展开更多
The optimal energy management for a plug-in hybrid electric bus(PHEB)running along the fixed city bus route is an important technique to improve the vehicles’fuel economy and reduce the bus emission.Considering the i...The optimal energy management for a plug-in hybrid electric bus(PHEB)running along the fixed city bus route is an important technique to improve the vehicles’fuel economy and reduce the bus emission.Considering the inherently high regularities of the fixed bus routes,the continuous state Markov decision process(MDP)is adopted to describe a cost function as total gas and electric consumption fee.Then a learning algorithm is proposed to construct such a MDP model without knowing the all parameters of the MDP.Next,fitted value iteration algorithm is given to approximate the cost function,and linear regression is used in this fitted value iteration.Simulation results show that this approach is feasible in searching for the control strategy of PHEB.Simultaneously this method has its own advantage comparing with the CDCS mode.Furthermore,a test based on a real PHEB was carried out to verify the applicable of the proposed method.展开更多
基金supported by National Natural Science Foundation of China(No.51467008)Gansu Provincial Department of Education Industry Support Program(No.2021CYZC-32)。
基金supported by the National Key Research and Development Project of China (No. 2017YFC1500301)the Joint Earthquake Research Program of the National Natural Science Foundation and the China Earthquake Administration (No. U1839206)the National Natural Science Foundation of China (No. 41974114)
文摘In this study,we implement forward modeling and inversion based on deep-learning strategies using an optimal nearly analytic discrete(ONAD)method.The forward-modeling method combines the ONAD method with recurrent neural network(RNN)for the fi rst time.RNN is a type of neural network that is suitable for sequential data,which uses information from both previous and current times to obtain output information.We express the ONAD method using an RNN framework to advance the time iteration of an acoustic equation.This process can simplify programming using RNN and convolution kernels.Next,we use deep learning based on the proposed forward-modeling method to study full waveform-inversion problems.Because the main purpose of inversion is to minimize the error between real and synthetic data,inversion is essentially an optimization problem.Many new optimizers are available in the framework of deep learning,such as the Adam and Nadam optimizers,which are used for optimizing velocity model in the inversion process.We perform six numerical experiments.The first two experiments demonstrate the forward-modeling results,which indicate that the forward-modeling method can effectively suppress numerical dispersion and improve computational effi ciency.The other four experiments demonstrate the inversion results,which show that the method proposed in this paper can eff ectively realize inversion imaging.We compare several optimizers used in deep learning and find that the Nadam optimizer has faster convergence and better effectiveness based on the ONAD method combined with RNN.
文摘In this paper, we report in-depth analysis and research on the optimizing computer network structure based on genetic algorithm and modified convex optimization theory. Machine learning method has been widely used in the background and one of its core problems is to solve the optimization problem. Unlike traditional batch algorithm, stochastic gradient descent algorithm in each iteration calculation, the optimization of a single sample point only losses could greatly reduce the memory overhead. The experiment illustrates the feasibility of our proposed approach.
基金supported by the National Natural Science Foundation of China(Grant No.51275557)the National Science-technology Support Plan Projects of China(Grant No.2013BAG14B01)
文摘The optimal energy management for a plug-in hybrid electric bus(PHEB)running along the fixed city bus route is an important technique to improve the vehicles’fuel economy and reduce the bus emission.Considering the inherently high regularities of the fixed bus routes,the continuous state Markov decision process(MDP)is adopted to describe a cost function as total gas and electric consumption fee.Then a learning algorithm is proposed to construct such a MDP model without knowing the all parameters of the MDP.Next,fitted value iteration algorithm is given to approximate the cost function,and linear regression is used in this fitted value iteration.Simulation results show that this approach is feasible in searching for the control strategy of PHEB.Simultaneously this method has its own advantage comparing with the CDCS mode.Furthermore,a test based on a real PHEB was carried out to verify the applicable of the proposed method.
