摘要
提出一种基于自主发育神经网络的机器人室内场景识别方法.利用3层自组织发育神经网络构建脑智模型.在发育阶段为了模拟神经元侧抑制效应,采用top-k竞争机制,获胜的神经元更新相应的突触权重向量,学习过程采用叶分量分析算法(lobe component analysis,LCA).在神经元突触得到加强之后,可以根据当前环境信息得到相应的思维结论,从而实现移动机器人对室内场景的自主识别.机器人通过类人思维方式,将学习结果以"知识"的形式储存,其思维结论得益于以往知识的积累.实验结果证明,作为"知识"的载体,文中所提的自主发育神经网络模型完全满足机器人场景识别任务需要,同时也实现了机器人对自己"所见"的学习、理解和成长.
A method of robot indoor scene recognition based on autonomous developmental neural network is proposed. 3-layer adaptive developmental neural network is used to build the brain-mind model. In developing phase, top-k competition is utilized to simulate the lateral inhibition of neurons, and the winner updates the synapse weight vector with the lobe component analysis（LCA） algorithm. The strengthened neurons can get thinking results according to current environment information, and indoor scenes can be recognized autonomously by mobile robots. Through human-like thinking, the learning results are stored as ＂knowledge＂, and the thinking results are derived from experience. Experimental results show that the model of autonomous developmental neural network proposed as the carrier of ＂knowledge＂, fully meets the need of indoor scenes recognition task, and realizes the autonomous learning, understanding and growth of robots based on vision.
出处
《机器人》
EI
CSCD
北大核心
2013年第6期703-708,743,共7页
Robot
基金
国家863计划资助项目(2006AA04Z246)
教育部重大创新工程培育资金资助项目(708045)
关键词
自主发育神经网络
叶分量分析算法
机器人
场景识别
autonomous developmental neural network
LCA（lobe component analysis）
robot
scene recognition
