A Hybrid Simulated Kalman Filter - Gravitational Search Algorithm (SKF-GSA)

被引：0

作者：

Muhammad, Badaruddin ^{[1
]}

Ibrahim, Zuwairie ^{[1
]}

Jusof, Mohd Falfazli Mat ^{[1
]}

Ab Aziz, Nor Azlina ^{[2
]}

Aziz, Nor Hidayati Abd ^{[2
]}

Mokhtar, Norrima ^{[3
]}

机构：

[1] Univ Malaysia Pahang, Fac Elect & Elect Engn, Pekan 26600, Pahang, Malaysia

[2] Multimedia Univ, Fac Engn & Technol, Melaka 75450, Malaysia

[3] Univ Malaya, Fac Engn, Kuala Lumpur 50603, Malaysia

来源：

ICAROB 2017: PROCEEDINGS OF THE 2017 INTERNATIONAL CONFERENCE ON ARTIFICIAL LIFE AND ROBOTICS | 2017年

关键词：

hybrid; simulated Kalman filter; gravitational search algorithm; CEC2014 benchmark problem;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, simulated Kalman filter (SKF) and gravitational search algorithm (GSA) are hybridized in such a way that GSA is employed as prediction operator in SKF. The performance is compared using CEC2014 benchmark dataset. The proposed hybrid SKF-GSA shown to perform better than individual SKF and GSA algorithm.

引用

页码：P707 / P710

页数：4