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Wang F.,Nanjing Army Command College | Chen L.,PLA Automobile Management Institute
Proceedings of the 2nd International Conference on Intelligent Control and Information Processing, ICICIP 2011 | Year: 2011

Maneuver is one of CGF action. For making CGF successfully maneuver in complex battlefield environment, firstly, the model of battlefield environment was established in the way of the rasterization. Second, the CGF model was established according to the maneuver description, and various CGF' behaviors including planning, movement, dodge, pursuit, escape, target track and so on, were considered as keys to be studied on this base. Third, in order to do with the route planning for CGF maneuver in the battlefield, the fitness function of genetic algorithm was improved. Forth, according to the course of the maneuver, the algorithm process of CGF maneuver planning was designed and explained. On these bases, the way of CGF maneuver planning was verified through an simulation experiment of maneuver based on a scenario. Finally, the research perspective of CGF maneuver planning was directed. © 2011 IEEE. Source


Xia T.,PLA University of Science and Technology | Wang X.-Q.,PLA University of Science and Technology | Liang S.,PLA University of Science and Technology | Dang X.-Z.,Transportation Institute | Wang J.-H.,PLA Automobile Management Institute
Jiefangjun Ligong Daxue Xuebao/Journal of PLA University of Science and Technology (Natural Science Edition) | Year: 2011

A new particle swarm optimization algorithm with adaptive genetic operator (AG-PSO) for training ANN was proposed to solve the problems appeared in the train of artificial neural network (ANN) such as the local minimum's basin of attraction and low speed. Controlled by probability, the particles were operated by genetic operator when ANN is trained by PSO algorithm. This new algorithm was used to train the ANN model of vehicle engine fault diagnosis. The result shows that the neural network trained by AG-PSO algorithm needs least amounts of iterations and achieves the better training accuracy than BP algorithm, GA and PSO algorithm. Source


Zhang Q.-Y.,PLA Automobile Management Institute | Qiu G.-Q.,PLA University of Science and Technology | Kou X.-Z.,PLA Automobile Management Institute | Chen L.,PLA University of Science and Technology
Jiefangjun Ligong Daxue Xuebao/Journal of PLA University of Science and Technology (Natural Science Edition) | Year: 2011

To improve the ability to TSP solve using GA, firstly a kind of definition of the population diversity was put forward, and then one kind of two-stage GA, setting two critical values in order to switch between greedy optimization GA and annealing partheno GA, was proposed to optimize the population in a large scale on the basis of keeping the population diversity. In the two-stage genetic algorithm, when the population diversity was degraded to some degree, it switched to the other algorithm searching the best result and improved the population diversity quickly. When the population diversity was ascended to some degree, it changed to the old algorithm, and this process repeated. The simulative result shows that the two-stage GA's convergence velocity and the searching capability are greatly improved, and that the average optimal result, the average convergence generations and the average running time are superior or the same as those of the other two GAs. Source


Wang F.,Nanjing Army Command College | Chen L.,PLA Automobile Management Institute | Lu H.-Q.,PLA University of Science and Technology
Jiefangjun Ligong Daxue Xuebao/Journal of PLA University of Science and Technology (Natural Science Edition) | Year: 2011

To properly handle CGF maneuver planning in the complex battlefield environment, a method of CGF maneuver planning based on genetic algorithm was studied. First, according to the characteristic of CGF maneuver, the model of battlefield environment was established with the grid way. Second, the maneuver CGF was designed, and various behaviors of CGF were considered as keys to be studied. Third, after confirming the population coding method and the fitness function, an improved genetic algorithm for CGF maneuver path planning was proposed by optimizing various genetic operators, and then the algorithm flow was established. Finally, after establishing the process flow of CGF maneuver planning, a experimental platform based on the Netlogo was used to examine the method. Results indicate that the method is reasonable and efficient, and can properly handle CGF maneuver planning in the complex battlefield environment. Source

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