LEARNING IN THE RECURRENT RANDOM NEURAL NETWORK

被引：261

作者：

GELENBE, E

机构：

来源：

NEURAL COMPUTATION | 1993年 / 5卷 / 01期

关键词：

D O I：

10.1162/neco.1993.5.1.154

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The capacity to learn from examples is one of the most desirable features of neural network models. We present a learning algorithm for the recurrent random network model (Gelenbe 1989, 1990) using gradient descent of a quadratic error function. The analytical properties of the model lead to a ''backpropagation'' type algorithm that requires the solution of a system of n linear and n nonlinear equations each time the n-neuron network ''learns'' a new input-output pair.

引用

页码：154 / 164

页数：11

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