基于粒子群变异的克隆选择算法的结构故障检测与分类
文献类型：期刊论文
| 作者 | 周悦; 岳林蓓; 张力心; 臧传治
|
| 刊名 | 信息与控制
 |
| 出版日期 | 2015 |
| 卷号 | 44期号:4页码:436-441 |
| 关键词 | 结构故障 克隆选择 粒子群 变异 |
| ISSN号 | 1002-0411 |
| 其他题名 | Structural Damage Detection and Classification Based on Clone Selection Algorithm of Particle Swarm Mutation |
| 产权排序 | 3 |
| 中文摘要 | 借鉴生物免疫系统异常识别能力相关研究,基于具有自治、自适应和演化能力的人工免疫理论与方法,对大型结构健康监测中的故障检测和分类问题进行研究,提出一种基于粒子群优化变异的克隆选择算法.该算法将样本结构模式数据作为抗原刺激抗体集合,抗体集合经过克隆、变异、选择等学习和进化过程以提高记忆细胞质量,以实现对实测数据的故障检测与分类.特别是针对克隆选择算法二进制编码复杂和变异方向不确定的问题,引入了粒子群优化变异.在Benchmark结构模型上的仿真实验结果表明该算法有效地识别故障模式,且提高了结构故障分类的成功率. |
| 英文摘要 | Inspired by a study on the ability of biological immune systems to identify antigens，we investigated a damage detection and classification problem in the health monitoring of large-scale structures and proposed a clone selection algorithm of particle swarm mutation based on the autonomous，adaptive，and evolutional artificial immune theory and method． The algorithm sampled data from a structure model of an antigen that stimulates antibody sets． To improve the quality of the memory cells and achieve damage detection and classification of the measured data，the antibodies go through a learning and evolving process that included cloning，mutation，and selection． In particular，particle swarm mutation was introduced to solve the problem of binary encoding complexity and the uncertainty of the mutation direction in the clone selection algorithm． The experimental results obtained for the proposed algorithm using the Benchmark structure model indicated that the algorithm could effectively identify failure modes and improve the success rate of structure damage classification． |
| 收录类别 | CSCD |
| 语种 | 中文 |
| CSCD记录号 | CSCD:5542537 |
| 公开日期 | 2015-12-27 |
| 源URL | [http://ir.sia.ac.cn/handle/173321/17429]  |
| 专题 | 沈阳自动化研究所_工业控制网络与系统研究室 |
| 推荐引用方式 GB/T 7714 | 周悦,岳林蓓,张力心,等. 基于粒子群变异的克隆选择算法的结构故障检测与分类[J]. 信息与控制,2015,44(4):436-441. |
| APA | 周悦,岳林蓓,张力心,&臧传治.(2015).基于粒子群变异的克隆选择算法的结构故障检测与分类.信息与控制,44(4),436-441. |
| MLA | 周悦,et al."基于粒子群变异的克隆选择算法的结构故障检测与分类".信息与控制 44.4(2015):436-441. |
入库方式： OAI收割
来源：沈阳自动化研究所
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