On weights and structure determination of Hermite-interpolation neural network
Hermite插值神经网络权值和结构确定理论探讨*

In order to overcome the inherent drawbacks of BP neural network, based on the Hermite-interpolation theory,this paper constructed a novel type of feed-forward neural-network model,which could be termed as Hermite-interpolation neural-network model.For this model,it presented a pseudo-inverse based weights determination method (or termed,weights-direct-determination method);and further investigated the determination of the hidden-layer neuron number(i.e.,structure-automatic-determination method).Computer-simulation results demonstrate that the presented Hermite-interpolation neural network with the above two methods can converge faster,and has a better testing performance(as compared to BP neural network), as well as the great de-noising and forecasting capabilities.