The Application of Chaotic BP Neural Network in Underwater Terrain matching navigation
As the traditional ICP algorithm is liable to get local minimization problem, a chaotic BP neural network is presented in the ICP algorithm. In the algorithm, a searching area of real position was plotted centering on the indication of refer navigation system, then terrain altitude data was extracted from refer terrain map. These terrain data, along with corresponding position coordinates, were defined as several patterns and used to train BP network. The network can recognizes certain pattern class with measured water-depth data to determine vehicles location. However, there are drawbacks of local minimization problem and slow rapidity of convergence in BP network, so improved ways were put forward. The improvement includes replacing common motivating function with chaotic motivating function for and determination of neural networks weights using chaotic search. The experimental results reveal that results of terrain matching can be improved, and matching failure caused by local convergence is overcome to a certain extent.
terrain matching ICP algorithm BP neural networks chaotic motivating function
ZHANG Tao XU Xiao-su
Department of Instrument Science & Engineering, Southeast University, Nanjing 210096
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
695-698
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）