ART2 Neural Networks with More Vigorous Vigilance Test Criterion
具有更严格警戒测试准则的ART2神经网络

The neural network models based on adaptive resonance theory(ART) are capable of organizing stable recognition categories for arbitrary binary or real imput patterns. However the ART neural networks are not sensitive to the distinguishing of those categories which there are only a few components in obvious difference between them. A new ART2 neural network model with more vigorous vigilance test criterion is proposed in this paper. The modified intercepted hyperbola function is adopted in the new vigilance test criterion to calculate the matching degree between the input vector and the weight vector of top to bottom. On one hand, the hyperbola function reduces the effect of noises and on the other hand, it emphasizes the effect of those components of input vector which have impulsive differences to the corresponding components of weight vector. The palm image recognition using the ART neural networks with the new vigilance test criterion has been carried out in this paper, the experiment results shows that the new vigilance test criterion is more robust to noises, and the new ART2 neural network can gain higher recognition rate under lower SNR.