初始权值优化技术在机器人学习中的应用

Implementation of Optimal Weights Initialization Technology in Robot Learning

针对移动机器人建立了基于BP神经网络的智能避障控制模型,提出了初始权值优化技术,使得样本组与初始权值相匹配,显著地提高了网络的收敛速度.为了提高系统的实时性,文中采用C和汇编语言混合编制控制程序.计算机仿真和实测结果表明该系统具有学习能力强、人机交互效果好等优点.

An intelligent obstacle avoidance model based on BP neural network is established,Also a novel optimal weights initialization technology is proposed so that the sample sets and initial weights can match perfectly. Consequently, the convergence speed increases evidently. In order to improve the real-time performance, hybrid programming using C and assemble language is adopted. Computer simulation and real test show that the system has a strong ability of learning and good performance of human computer interaction.