峭度自适应学习率的盲信源分离  

Blind Source Separation with Kurtosis Adaptive Learning Rate

作　　者：孙守宇[1,2] 郑君里[1,2] 吴里江 赵莹[1,2] 

机构地区：[1]清华大学电子工程系,北京100084 [2]北京信息技术研究所,北京100088

出　　处：《电子学报》2005年第3期473-476,共4页Acta Electronica Sinica

摘　　要：本文提出了一种自适应学习率盲信源分离的自然梯度算法 ,自适应学习率仅依赖于神经网络输出峭度平方和的负指数 .开始阶段由于小的峭度 ,学习率大收敛速度快 .之后 ,随着峭度变大 ,学习率慢慢变小 ,产生小的稳态误差 .在线性无记忆混合的情况下 ,用欠高斯信源进行的模拟实验表明 ,与固定学习率相比 ,本文提出的峭度自适应学习率盲信源分离算法具有收敛速度快和稳态误差小的特点 .A natural gradient algorithm with an adaptive learning rate for blind source separation is proposed. The adaptive learning rate only depends on the negative exponential of the square sum of the kurtosis of neural networks outputs. The initial convergence speed is very fast because of large learning rate (according to small kurtosis). After an initial period, the learning rate decreases slowly due to large kurtosis, giving rise to small steady-state errors. Simulation with under-Gaussian as sources and in the instantaneous mixing case shows that the proposed algorithm has faster convergence and smaller steady-state error than those without adaptive learning rate.

关 键 词：自适应 学习率 盲信源分离 峭度 

分 类 号：TN911[电子电信—通信与信息系统]