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39th Chinese Control Conference (CCC)

作者： Gao Xinzhou Qu Yaobin Li Wenbo Ma Guangfu Han Peng Harbin Inst Technol Dept Control Sci & Engn Harbin 150001 Peoples R China Shanghai Inst Satellite Engn Shanghai 201109 Peoples R China Beijing Inst Control Engn Beijing 100190 Peoples R China

ISBN: (数字)9789881563903

ISBN: (纸本)9789881563903

When the Agile Earth Observation Satellite(AEOS) observes ground target points, the properties of the ground target points often change, which requires the satellite to effectively re-plan its mission in a short time. In order to solve the problem of mission re-planning of the AEOS, an improved genetic algorithm is proposed in this paper. Firstly, the fitness function to be optimized is established according to the satellite's constraints. The benefits of the satellite observation, the constraints of the satellite and the invariant target points included in the re-planned observation sequence is considered in this fitness function. These constraints mainly include time constraints, energy constraints, satellite orbital dynamic constraints, and so on. Secondly, considering the problems faced in the mission re-planning process, such as the satellite's need to complete the mission re-planning in a short time, and the constraints it faces, etc., the adaptive mutation genetic algorithm(AMGA) is proposed in this paper. Finally, simulation experiments verify that AMGA can complete mission re-planning while meeting various constraints, and that AMGA meets the fast and accurate requirements for solving mission re-planning problems.

关键词： Mission Re-planning Agile Earth Observation Satellite adaptive mutation genetic algorithm

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Mission Re-planning for Agile Earth Observation Satellite Using adaptive mutation genetic algorithm

Mission Re-planning for Agile Earth Observation Satellite Us...

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第三十九届中国控制会议

作者： Gao Xinzhou Qu Yaobin Li Wenbo MA Guangfu Han Peng Department of Control Science and Engineering Harbin Institute of Technology Shanghai Institute of Satellite Engineering Beijing Institute of Control Engineering

When the Agile Earth Observation Satellite（AEOS） observes ground target points, the properties of the ground target points often change, which requires the satellite to effectively re-plan its mission in a short time. In order to solve the problem of mission re-planning of the AEOS, an improved genetic algorithm is proposed in this paper. Firstly, the fitness function to be optimized is established according to the satellite’s constraints. The benefits of the satellite observation, the constraints of the satellite and the invariant target points included in the re-planned observation sequence is considered in this fitness function. These constraints mainly include time constraints, energy constraints, satellite orbital dynamic constraints, and so on. Secondly,considering the problems faced in the mission re-planning process, such as the satellite’s need to complete the mission re-planning in a short time, and the constraints it faces, etc., the adaptive mutation genetic algorithm（AMGA） is proposed in this ***, simulation experiments verify that AMGA can complete mission re-planning while meeting various constraints, and that AMGA meets the fast and accurate requirements for solving mission re-planning problems.

关键词： Mission Re-planning Agile Earth Observation Satellite adaptive mutation genetic algorithm

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The improved genetic and BP hybrid algorithm and neural network economic early warning system

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NEURAL COMPUTING & APPLICATIONS 2022年第5期34卷 3365-3374页

作者： Yin, Xinzhe Li, Jinghua Huang, Shoujun Sichuan Int Studies Univ Sch Int Business Chongqing 400031 Peoples R China Univ Sci & Technol China Sch Publ Affairs Hefei 230026 Peoples R China

Economic early warning is the recognition and judgment of the state of economic operation, and its research results directly affect the rational formulation of macro-control policies. However, the traditional early warning methods are mainly based on expert experience or simple statistical model, which are difficult to reflect the nature of highly nonlinear economic system and can not meet the objective requirements of macroeconomic early warning. Based on the above background, the purpose of this study is to design an economic early warning system based on improved genetic and BP hybrid algorithm and neural network. Based on the overview of macroeconomic early warning at home and abroad, this study expounds the design of early warning index system, early warning model, establishment of early warning system and other issues in the macroeconomic early warning theoretical system;deeply analyses the theoretical methods of BP neural network and adaptive mutation genetic algorithm, and discusses the feasibility of realizing macroeconomic early warning by BP neural network and adaptive mutation genetic algorithm, The improved genetic and BP hybrid algorithm and neural network economic early warning model are established. Finally, the experimental results show that the correlation coefficient between the composite index and the comprehensive early warning is 0.89, and the delay number is 0, which shows that the early warning index obtained by the early warning system can accurately reflect the actual economic fluctuations. The results show that the improved genetic and BP hybrid algorithm and neural network economic early warning system are effective, feasible and have good accuracy.

关键词： Economic early warning BP algorithm adaptive mutation genetic algorithm Neural network

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