AN ASSOCIATION-LEARNING-MEMORY NEURAL NETWORK MODEL NEAR SATURATION STATE OF STORAGE
近存储饱和状态下联想学习记忆的神经网络模型

This paper Presents a new neural network model near saturation state of storage and discusses main properties of the model on association and learning.Calculations of computer simulation to the system with 100 neurons and 10 random patterns are given and analysed. Discussion of the difference between the new model and Hebb's rule is presented, and the effects of initial noise Pi and association noise P. on the retrival of a learned pattern are also studied.Some conclusions are obtained.