A HIGHER-ORDER NEURAL NETWORK MODEL WITH NODE BIAS
具有节点偏置的高阶神经网络模型

This paper presented a higher -order neural network model with node bias.The Hamilton and the learning algoithm of the model were given. The convergence of the learning algorithm was proved. It introduced automatically a node bias to each neuron so that the new model could store all the learning patterns including coherent patterns, thus the storage capacity of the new model was much higher than that of higher -order model using Hebb -rul - e like learning algorithm. Calculations of computer simulation to a 2nd-order system with 30 neurons were carried out. The results confirmed the above conclusion. The relationship between learning effects and initial synapse weights and the relationship between average attraction radius and number of stored patterns were simulated and analysed. All these remarkable features enable the new model to be of good prospects in application.