兵工自动化2015，Vol.34Issue(5)：59-65,7.DOI:10.7690/bgzdh.2015.05.016
基于自组织增量学习神经网络的信息融合技术
Information Fusion Based on Self-organizing Incremental Neural Network
时晓峰 1申富饶 1贺红卫2
作者信息
- 1. 南京大学计算机科学与技术系软件新技术国家重点实验室,南京 210023
- 2. 中国兵器科学研究院,北京100089
- 折叠
摘要
Abstract
To solve the problems in information fusion with traditional neural networks, an information fusion method based on self-organizing incremental neural network (SOINN) is proposed. The proposed fusion system can receive the input data with any dimension and any format from different kinds of sensors. The incremental orthogonal component analysis (IOCA) method is used to reduce the dimensionality of data and extract features adaptively. Then the heterogeneous features are learnt by SOINN. During this period, the connected regions of neurons are generated based on the heterogeneous features and the associated relations between the neuron regions are built. By this way, the data fusion is realized at all of the data level, the feature level, and the decision level. It's shown from the experiments that the dimension can be reduced and the data recorded by different sensors can be learnt adaptively, and then the decisions and instructions of the robots are generated accordingly.关键词
智能机器人/信息融合/自组织增量学习神经网络/联想记忆Key words
intelligent robot/information fusion/self-organizing incremental neural network/associative memory分类
信息技术与安全科学引用本文复制引用
时晓峰,申富饶,贺红卫..基于自组织增量学习神经网络的信息融合技术[J].兵工自动化,2015,34(5):59-65,7.
