Hybrid self-configuring evolutionary algorithm for automated design of fuzzy logic rule base
In this paper a method for fuzzy logic systems design,which implements the latest developments in this field,is presented.The main evolutionary algorithm uses the Pittsburg-type approach,and the Michigan-type one is used as a mutation operator.A self-configuring technique is used to adjust the algorithm parameters based on their success rates.The novelty here is the algorithms ability to adjust the probability using either the genetic or heuristic method for the incorporation of a new rule in the rule base.Previously,this was done voluntarily.It is demonstrated that this new algorithms flexibility does not decrease its performance although it makes it fully automated.
fuzzy rule based classifiers evolutionary algorithms genetic fuzzy systems self-configuration
Vladimir Stanovov Eugene Semenkin
Department of System Analysis and Operations Research Siberian State Aerospace University Krasnoyarsk,Russia
国际会议
厦门
英文
325-329
2014-08-19（万方平台首次上网日期，不代表论文的发表时间）