Feedforward neural networks for compound signals

被引：17

作者：

Szczuka, Marcin ^{[1
]}

Slezak, Dominik ^{[1
,2
]}

机构：

[1] Univ Warsaw, Inst Math, PL-02097 Warsaw, Poland

[2] Infobright Inc Poland, PL-02078 Warsaw, Poland

来源：

THEORETICAL COMPUTER SCIENCE | 2011年 / 412卷 / 42期

关键词：

Multilayer neural network; Error backpropagation; Compound signal; Classification; Approximation; ROUGH; ALGORITHM;

D O I：

10.1016/j.tcs.2011.05.046

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

In this paper we consider possible extensions of the classical multilayer artificial neural network model to the situation when the signals processed by the network are by definition compound and possibly structured. We discuss existing approaches to this problem in various contexts and provide our own model - the Normalizing Neural Network - for networks that process vectors as signals. We discuss possible uses of the proposed approach in a series of case studies. (C) 2011 Elsevier B.V. All rights reserved.

引用

页码：5960 / 5973

页数：14

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