遗传算法优化神经网络权值盲均衡算法的研究  

Research of genetic algorithm optimization neural network weights blind equalization algorithm based on real number coding

作　　者：张立毅[1,2] 刘婷[1] 孙云山[1] 李锵[2] 

机构地区：[1]天津商业大学信息工程学院,天津300134 [2]天津大学电子信息工程学院,天津300072

出　　处：《计算机工程与应用》2009年第11期162-164,共3页Computer Engineering and Applications

基　　金：中国博士后基金,No.20060390170;天津市高校科技发展基金,No.20060610~~

摘　　要：将遗传算法与神经网络盲均衡算法相结合,提出了两段式优化神经网络权值的方案。首先利用遗传算法全局搜索能力强的特点优化初始权值,然后发挥BP算法局部搜索速度快的特点得到最佳权值。经计算机仿真表明,该算法与传统BP神经网络盲均衡算法相比,收敛速度加快,稳态剩余误差减小,误码率降低。The project of two-stage optimization neural network weights is proposed by combining genetic algorithm with neural network blind equalization algorithm.At first,the initialization weight is optimized using the characteristic of genetic algorithm, which is strong global search capability.And then,optimal weight is gained in virtue of the merit of BP algorithm,which is fast local search speed.Computer simulations show that,compared with traditional blind equalization algorithm based on BP neural network,the convergence speed of the proposed algorithm is quickened, state residual error is decreased and BER is reduced.

关 键 词：盲均衡算法 神经网络 遗传算法 初始权值 

分 类 号：TN911.5[电子电信—通信与信息系统]