摘要
为解决传统自适应遗传算法存在的不足,在实数编码策略和精英保留策略的基础上,提出了一种改进的自适应遗传算法,对遗传操作的交叉概率和变异概率进行了改进。将其应用于系统参数辨识,结果证明该算法具有更高的辨识精度和更强的抗噪声能力。
In order to solve the deficiency of the traditional adaptive genetic algorithm （AGA）, an improved adaptive genetic algorithm （IAGA） was presented on the basis of introduction to the real-coding strategy and the elitism strategy. The crossover probability and mutation probability of genetic operation were improved in this algorithm. With the algorithm applied to the system parameter identification, the result proves that this algorithm is in possession of higher identification precision and stronger anti-noise ability.
出处
《电工电气》
2010年第3期29-32,共4页
Electrotechnics Electric
基金
江苏省精密与微细制造技术重点实验室基金资助项目(JSPM200701)
关键词
自适应遗传算法
实数编码
精英保留
参数辨识
adaptive genetic algorithm
real-coding
elitism
parameter identification
