智能系统学报2015，Vol.10Issue(5)：755-761,7.DOI:10.11992/tis.201407031
双蝙蝠群智能优化的多模盲均衡算法
Multi-modulus blind equalization algorithm based on double bat swarms intelligent optimization
摘要
Abstract
Aiming at the defects of the large surplus mean square error and slow convergence speed in equalizing multi-modulus QAM signals by utilizing constant modulus algorithm ( CMA) , a multi-modulus blind equalization al-gorithm based on double bat swarms intelligent optimization ( DBSIO-MMA) is proposed. In the algorithm, a group of optimal position vectors attained by independent global optimization of two bat swarms are respectively taken as the real and imaginary parts of the initialized optimal weight vector, so as to improve convergence speed and reduce surplus mean square error. The simulation results show that the features of fast convergence speed and high success rate of the bat algorithm ( BA) in global search are fully reflected in the proposed algorithm. Compared with the CMA, multi-modulus blind equalization algorithm ( MMA) , particle swarm optimization based MMA ( PSO-MMA) and bat swarms intelligent optimization based MMA ( BA-MMA ) , the proposed algorithm has faster convergence speed and smaller mean square error.关键词
常模盲均衡算法/多模盲均衡算法/蝙蝠算法/全局最优位置/最优权向量Key words
constant modulus algorithm ( CMA )/multi-modulus blind equalization algorithm ( MMA )/bat algo-rithm ( BA)/global optimal position/optimal weight vector分类
信息技术与安全科学引用本文复制引用
郭业才,吴华鹏..双蝙蝠群智能优化的多模盲均衡算法[J].智能系统学报,2015,10(5):755-761,7.基金项目
江苏省高校自然科学基金重大资助项目(13KJA510001) （13KJA510001）
高校科研成果产业化推进工程资助项目( JHB 2012-9) （ JHB 2012-9）
全国优秀博士论文作者专项资金资助项目(200753) （200753）
江苏省高校"信息与通信工程"优势学科建设工程资助项目(2014). （2014）
