Paper Info
Title
A modified BBO algorithm and its application for identifying Hammerstein system under heavy-tailed noises
Abstract
The Evolutionary algorithm (EA) for researching parameters of nonlinear system is a rapidly growing field of identification. This can owe to the importance of EA for both the theoretical field and the engineering community. However, the identification of the nonlinear system is still a knotty problem, especially when heavy-tailed noises exists. Compared to classical identification methods, EA has more advantages as its fast searching speed and low complexity. In this paper, based on Biogeography Based Optimization (BBO) algorithm which was presented in recent years, a modified BBO (MBBO) is presented to overcome the premature drawback of BBO. A computing method of mutation ratio is added in MBBO to enhance exploration ability. Then, the MBBO algorithm is used for a Hammerstein model with a heavy-tailed perturbation. Experiment results verify that MBBO has much higher accuracy than BBO.
Year
DOI
Venue
2017
10.23919/IConAC.2017.8082040
2017 23rd International Conference on Automation and Computing (ICAC)
Keywords
Field
DocType
evolutionary algorithm,Biogeography Based Optimization algorithm,nonlinear system identification,heavy-tailed noises
Convergence (routing),Mathematical optimization,Algorithm design,Nonlinear system,Evolutionary algorithm,Algorithm,Biogeography-based optimization,Engineering,Probability density function,Genetic algorithm
Conference
ISBN
Citations 
PageRank 
978-1-5090-5040-6
0
0.34
References 
Authors
8
2
Name
Order
Citations
PageRank
Na Yang1194.49
Qibing Jin21911.28