Goh Su Lee, Mandic Danilo P
Neural Comput. 2007 Apr;19(4):1039-55. doi: 10.1162/neco.2007.19.4.1039.
An augmented complex-valued extended Kalman filter (ACEKF) algorithm for the class of nonlinear adaptive filters realized as fully connected recurrent neural networks is introduced. This is achieved based on some recent developments in the so-called augmented complex statistics and the use of general fully complex nonlinear activation functions within the neurons. This makes the ACEKF suitable for processing general complex-valued nonlinear and nonstationary signals and also bivariate signals with strong component correlations. Simulations on benchmark and real-world complex-valued signals support the approach.
介绍了一种针对实现为全连接递归神经网络的非线性自适应滤波器类的增强型复值扩展卡尔曼滤波器（ACEKF）算法。这是基于所谓增强型复统计量的一些最新进展以及神经元内通用全复值非线性激活函数的使用而实现的。这使得ACEKF适用于处理一般复值非线性和非平稳信号以及具有强分量相关性的双变量信号。对基准和实际复值信号的仿真支持了该方法。
