Space moving object recognition and tracking is an important research topic in computer vision. It has broad application prospects in space exploration, detection of traffic flow, military field, automatic control and other fields. This paper aims to propose a new space target recognition algorithm, and use this algorithm to identify the motion trajectory simulation of a certain object in the universe.
Proc. SPIE. 10322, Seventh International Conference on Electronics and Information Engineering
KEYWORDS: Control systems design, Particle swarm optimization, Particles, Evolutionary algorithms, Neural networks, Control systems, Algorithm development, Detection and tracking algorithms, Error analysis, Microelectromechanical systems
The particle swarm optimization algorithm to improve the control precision, and has great application value training neural network and fuzzy system control fields etc.The traditional particle swarm algorithm is used for the training of feed forward neural networks,the search efficiency is low, and easy to fall into local convergence.An improved particle swarm optimization algorithm is proposed based on error back propagation gradient descent. Particle swarm optimization for Solving Least Squares Problems to meme group, the particles in the fitness ranking, optimization problem of the overall consideration, the error back propagation gradient descent training BP neural network, particle to update the velocity and position according to their individual optimal and global optimization, make the particles more to the social optimal learning and less to its optimal learning, it can avoid the particles fall into local optimum, by using gradient information can accelerate the PSO local search ability, improve the multi beam particle swarm depth zero less trajectory information search efficiency, the realization of improved particle swarm optimization algorithm. Simulation results show that the algorithm in the initial stage of rapid convergence to the global optimal solution can be near to the global optimal solution and keep close to the trend, the algorithm has faster convergence speed and search performance in the same running time, it can improve the convergence speed of the algorithm, especially the later search efficiency.