计算机科学 ›› 2011, Vol. 38 ›› Issue (10): 211-214.

• 人工智能 • 上一篇    下一篇

V正交基网络

熊刚强,齐东旭   

  1. (广东医学院信息工程学院 东莞523808);(中山大学信息科学与技术学院 广州510275);(澳门科技大学资讯科技学院 澳门)
  • 出版日期:2018-11-16 发布日期:2018-11-16

V Orthonormal Basis Neural Network

XIONG Gang-qiang,QI Dong-xu   

  • Online:2018-11-16 Published:2018-11-16

摘要: 为了改进BP网络的收敛速度与连续正交基网络无法逼近非连续函数的问题,构造了一类基于V正交基的 前馈神经网络(简称V正交基网络),并研究其收敛性条件与伪逆规则。由于V系统是厂(巨0,1习)上的一类完备的正 交函数系,且Fouricr-V级数有较快的收敛速度,因此,V正交基网络有较快的收敛速度,且能有效地逼近一类强间断 的一元函数。最后,通过仿真实验证明,V正交基网络的收敛速度明显优于传统的13P网络、小波网络与工cgcndrc网 络,特别是逼近一类间断点在二进制有理数处的函数时,其优势更加明显。

关键词: V系统,PP神经网络,小波神经网络,Legendre网络,函数逼近

Abstract: In order to solve the problem that the convergence rate of BP network is not fast, and the neural networks with continuous orthogonal basis cannot approximate discontinuous functions,this paper constructed a class of feed-for- ward neural networks with V orthonormal basis (referred to as V orthogonal network) , and investigated its convergence condition and pseudo-inverse rule. For V system is a class of complete orthonormal systems in I}z(巨。,1习),and the con- vergence rate of Fourier-V series is comparatively fast, the convergence rate of V orthogonal network is also fast, and it can effectively approximate a class of discontinuous functions of one variable. The simulation results also show that the convergence rate of V orthogonal network is obviously faster than that of 13P network, wavclet network and Legendre network;if using V orthogonal network to approximate the functions whose breakpoints only appear at dyadic rational, its performance of function approximation becomes much better.

Key words: V system, BP network, Wavclct network, I_cgcndrc network, Function approximation

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