Generalized radial basis function neural network based on an improved dynamic particle swarm optimization and AdaBoost algorithm

被引：60
|
作者：
Lu, Jinna [1 ,2 ]
Hu, Hongping [2 ]
Bai, Yanping [2 ]
机构：
[1] North Univ China, Sch Informat & Commun Engn, Taiyuan 030051, Shanxi, Peoples R China
[2] North Univ China, Dept Math, Taiyuan 030051, Shanxi, Peoples R China
来源：
NEUROCOMPUTING | 2015年 / 152卷
关键词：
Generalized radial basis function; Dynamic particle swarm optimization; Exponential decreasing inertia weight; AdaBoost algorithm; EXTREME LEARNING-MACHINE; PERFORMANCE; PREDICTION; PARAMETER;
D O I：
10.1016/j.neucom.2014.10.065
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper proposes an improved dynamic particle swarm optimization algorithm, which uses a new and effective exponential decreasing inertia weight (EDIW) strategy. Based on the improved EDIW-PSO algorithm together with AdaBoost algorithm, we adjust the parameters (centers, widths, shape parameters and connection weights) of GRBF and present a novel hybrid EDIW-PSO-AdaBoost-GRBF model. Two application examples are given on the proposed model. The results obtained show that the proposed model is effective and feasible for prediction problems. (C) 2014 Elsevier B.V. All rights reserved.
引用
收藏
页码：305 / 315
页数：11
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