过程神经元网络的理论研究与仿真

过程神经元网络是一种适合于处理过程式信号输入的网络,其基本单元是过程神经元——一种新的神经元模型.过程神经元和传统神经元既存在本质区别,又有着紧密的联系,前者可用后者以任意精度无限逼近.文中首先介绍了过程神经元及其网络模型;然后,给出了过程神经元的两个逼近定理及其证明——时域特征扩展模型和正交分解特征扩展模型.基于第二个定理,给出了数值输出型过程神经网络的相关推论.针对模拟信号的仿真实验表明,过程神经网络对白噪声具有很好的抑制作用.最后,针对过程神经网络面临的主要问题进行讨论,指出了一些具有前景的研究方向.

Theoretical Investigation and Simulation of Process Neural Networks

The process neural networks(PNNs) are networks that adapt to the process of signal input,whose elementary unit is the process neuron(PN),an emerging neuron model.Both essential difference and close correlation exist between the process neuron and the traditional neurons,for example,PN can be approximated by traditional neurons with arbitrary precision.In this paper,the PN model and some PNNs are introduced.Then,two PN approximating theorems are presented and proved in detail.Each theorem gives an approximating model to the PN model,i.e.,the time-domain feature expansion model and the orthogonal decomposition feature expansion model.Moreover,a corollary is given for the real-valued output PNN based on the second theorem.Afterwards,a simulation of analog signals is carried out,showing that the PNN can well suppress the white noises contained in signals.Finally,some problems about PNNs are discussed and further research orientations are suggested.