Parallel Learning of Feedforward Neural Networks Without Error Backpropagation

被引：14

作者：

Bilski, Jaroslaw ^{[1
]}

Wilamowski, Bogdan M. ^{[2
]}

机构：

[1] Czestochowa Tech Univ, Inst Computat Intelligence, Czestochowa, Poland

[2] Auburn Univ, Auburn, AL 36849 USA

来源：

ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, ICAISC 2016 | 2016年 / 9692卷

关键词：

Forward-only computation; Neural network training; Parallel architectures; REALIZATION; ALGORITHM;

D O I：

10.1007/978-3-319-39378-0_6

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A parallel architecture of the steepest descent algorithm for training fully connected feedforward neural networks is presented. This solution is based on a new idea of learning neural networks without error backpropagation. The proposed solution is based on completely new parallel structures to effectively reduce high computational load of this algorithm. Detailed parallel 2D and 3D neural network learning structures are explicitely discussed.

引用

页码：57 / 69

页数：13

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