As to the fact that it is difficult to obtain analytical form of optimal sampling density and tracking performance of standard particle probability hypothesis density(P-PHD) filter would decline when clustering algori...As to the fact that it is difficult to obtain analytical form of optimal sampling density and tracking performance of standard particle probability hypothesis density(P-PHD) filter would decline when clustering algorithm is used to extract target states,a free clustering optimal P-PHD(FCO-P-PHD) filter is proposed.This method can lead to obtainment of analytical form of optimal sampling density of P-PHD filter and realization of optimal P-PHD filter without use of clustering algorithms in extraction target states.Besides,as sate extraction method in FCO-P-PHD filter is coupled with the process of obtaining analytical form for optimal sampling density,through decoupling process,a new single-sensor free clustering state extraction method is proposed.By combining this method with standard P-PHD filter,FC-P-PHD filter can be obtained,which significantly improves the tracking performance of P-PHD filter.In the end,the effectiveness of proposed algorithms and their advantages over other algorithms are validated through several simulation experiments.展开更多
For multi-agent reinforcement learning in Markov games, knowledge extraction and sharing are key research problems. State list extracting means to calculate the optimal shared state path from state trajectories with c...For multi-agent reinforcement learning in Markov games, knowledge extraction and sharing are key research problems. State list extracting means to calculate the optimal shared state path from state trajectories with cycles. A state list extracting algorithm checks cyclic state lists of a current state in the state trajectory, condensing the optimal action set of the current state. By reinforcing the optimal action selected, the action policy of cyclic states is optimized gradually. The state list extracting is repeatedly learned and used as the experience knowledge which is shared by teams. Agents speed up the rate of convergence by experience sharing. Competition games of preys and predators are used for the experiments. The results of experiments prove that the proposed algorithms overcome the lack of experience in the initial stage, speed up learning and improve the performance.展开更多
Land use change is a very complex process of evolution.On the basis of the principle of cellular automata,this article presents a kind of method that we can first mine state transition rule from historical map data,an...Land use change is a very complex process of evolution.On the basis of the principle of cellular automata,this article presents a kind of method that we can first mine state transition rule from historical map data,and then conduct forecast by virtue of Monte-Carlo method,achieving spatial dynamic forecast from map to map.We interpret TM remote sensing image in Ji'nan City in 2004 and 2006 to get present land use map for empirical research,and forecast land use map in 2012 and 2016,respectively.Studies show that this method of using spatial data to mine state transition rule,has advantages of simpleness,accuracy,strong real-time characteristic etc.in the simulation of dynamic change of land use,the results of which are roughly in line with the actual results,therefore,it can provide reference for land use planning.展开更多
文摘As to the fact that it is difficult to obtain analytical form of optimal sampling density and tracking performance of standard particle probability hypothesis density(P-PHD) filter would decline when clustering algorithm is used to extract target states,a free clustering optimal P-PHD(FCO-P-PHD) filter is proposed.This method can lead to obtainment of analytical form of optimal sampling density of P-PHD filter and realization of optimal P-PHD filter without use of clustering algorithms in extraction target states.Besides,as sate extraction method in FCO-P-PHD filter is coupled with the process of obtaining analytical form for optimal sampling density,through decoupling process,a new single-sensor free clustering state extraction method is proposed.By combining this method with standard P-PHD filter,FC-P-PHD filter can be obtained,which significantly improves the tracking performance of P-PHD filter.In the end,the effectiveness of proposed algorithms and their advantages over other algorithms are validated through several simulation experiments.
基金supported by the National Natural Science Foundation of China (61070143 61173088)
文摘For multi-agent reinforcement learning in Markov games, knowledge extraction and sharing are key research problems. State list extracting means to calculate the optimal shared state path from state trajectories with cycles. A state list extracting algorithm checks cyclic state lists of a current state in the state trajectory, condensing the optimal action set of the current state. By reinforcing the optimal action selected, the action policy of cyclic states is optimized gradually. The state list extracting is repeatedly learned and used as the experience knowledge which is shared by teams. Agents speed up the rate of convergence by experience sharing. Competition games of preys and predators are used for the experiments. The results of experiments prove that the proposed algorithms overcome the lack of experience in the initial stage, speed up learning and improve the performance.
基金Supported by National Natural Science Foundation(40571119)Shandong Natural Science Foundation(Y2007E05)
文摘Land use change is a very complex process of evolution.On the basis of the principle of cellular automata,this article presents a kind of method that we can first mine state transition rule from historical map data,and then conduct forecast by virtue of Monte-Carlo method,achieving spatial dynamic forecast from map to map.We interpret TM remote sensing image in Ji'nan City in 2004 and 2006 to get present land use map for empirical research,and forecast land use map in 2012 and 2016,respectively.Studies show that this method of using spatial data to mine state transition rule,has advantages of simpleness,accuracy,strong real-time characteristic etc.in the simulation of dynamic change of land use,the results of which are roughly in line with the actual results,therefore,it can provide reference for land use planning.
