基于累积量的两层前馈神经网络盲辨识

戴宪华

基于累积量的两层前馈神经网络盲辨识

戴宪华

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出版历程
- 收稿日期: 1999-09-13
- 修回日期: 2000-07-07
- 刊出日期: 2002-01-19

Cumulant-bansed blind identification of two-layer feedforward neural networks

Dai Xianhua

摘要

摘要: 由于非线性系统输出是其参数的非线性函数,直接利用高阶累积量辨识两层前馈神经网络(FNN)通常是十分困难的。为解决这一问题,该文提出两种基于四阶累积量的FNN辨识方法。第一种方法,FNN的隐元在其输入空间利用多个线性系统近似,进而FNN利用一统计模型混合专家(ME)网络重新描述。基于ME模型,FNN参数可利用统计期望值最大化(EM)算法获得估计。第二种方法,为简化FNN的ME模型,引入隐含观测量。基于隐含观测量估计,FNN被分解为多个单隐元的训练问题,进而整体FNN可利用一两阶层ME描述。基于单隐元的参数估计,FNN可利用一具有更快收敛速度的简化算法获得估计。
- ME模型; EM算法; 基于累积量的系统辨识
Abstract: It is always difficult to train a two-layer Feedforward Neural Networks (FNN) based on the cumulant match criterion because cumulants are nonlinear implicit functions of the FNN parameters. In this work, two new cumulant-based training methods for two-layer FNN are developed. In the first method, the hidden units of two-layer FNN are approximated with multiple linear systems, and further total FNN is modeled with a mixture of experts (ME) architecture. With the ME model, FNN parameters are estimated with expectation-maximization (EM) algorithm. The second method, for simplifying the two-layer FNN statistical model, proposes a simplified two-level hierarchical ME to remodel the FNN, in which hidden variables are introduced such that training total FNN is changed to training a set of single neurons. Based on training single neuron, total FNN is estimated in a simplified version with a faster convergence speed.

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参考文献(1)

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