ART-2 neural network based on eternal term memory vector:Architecture and algorithm  

作　　者：赵学智 叶邦彦 

机构地区：[1]School of Mechanical and Automotive Engineering, South China University of Technology

出　　处：《Journal of Harbin Institute of Technology(New Series)》2009年第6期843-848,共6页哈尔滨工业大学学报（英文版）

基　　金：Sponsored by the National Natural Science Foundation of China (Grant No. 50305005)

摘　　要：Aimed at the problem that the traditional ART-2 neural network can not recognize a gradually changing course, an eternal term memory (ETM) vector is introduced into ART-2 to simulate the function of human brain, i.e. the deep remembrance for the initial impression.. The eternal term memory vector is determined only by the initial vector that establishes category neuron node and is used to keep the remembrance for this vector for ever. Two times of vigilance algorithm are put forward, and the posterior input vector must first pass the first vigilance of this eternal term memory vector, only succeeded has it the qualification to begin the second vigilance of long term memory vector. The long term memory vector can be revised only when both of the vigilances are passed. Results of recognition examples show that the improved ART-2 overcomes the defect of traditional ART-2 and can recognize a gradually changing course effectively.Aimed at the problem that the traditional ART-2 neural network can not recognize a gradually changing course, an eternal term memory （ETM） vector is introduced into ART-2 to simulate the function of human brain, i.e. the deep remembrance for the initial impression.. The eternal term memory vector is determined only by the initial vector that establishes category neuron node and is used to keep the remembrance for this vector for ever. Two times of vigilance algorithm are put forward, and the posterior input vector must first pass the first vigilance of this eternal term memory vector, only succeeded has it the qualification to begin the second vigilance of long term memory vector. The long term memory vector can be revised only when both of the vigilances are passed. Results of recognition examples show that the improved ART-2 overcomes the defect of traditional ART-2 and can recognize a gradually changing course effectively.

关 键 词：ART-2 neural network eternal term memory vector two times of vigilance gradually changing course pattern recognition 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] Q782[自动化与计算机技术—控制科学与工程]