Learning of geometric mean neuron model using resilient propagation algorithm

被引：16
|
作者：
Shiblee, Md. [1 ]
Chandra, B. [2 ]
Kalra, P. K. [1 ]
机构：
[1] Indian Inst Technol, Dept Elect Engn, Kanpur, Uttar Pradesh, India
[2] Indian Inst Technol, Dept Ind Engn & Management, Kanpur, Uttar Pradesh, India
来源：
EXPERT SYSTEMS WITH APPLICATIONS | 2010年 / 37卷 / 12期
关键词：
Neuron model; Geometric mean; Resilient propagation; Functional approximation; MULTILAYER FEEDFORWARD NETWORKS;
D O I：
10.1016/j.eswa.2010.04.018
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The paper proposes a new neuron model (geometric mean neuron model) with an aggregation function based on geometric mean of all inputs. Performance of the geometric mean neuron model was evaluated using various learning algorithms like the back-propagation and resilient propagation on various real life data sets. Comparison of the performance of this model was made with the performance of multilayer perceptron. It has been shown that the geometric mean based aggregation function with resilient propagation (RPROP) performs the best both in terms of accuracy and speed. (C) 2010 Elsevier Ltd. All rights reserved.
引用
收藏
页码：7449 / 7455
页数：7
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