Adaptive filter theory
Fast RLS-Like Algorithm for Generalized Eigendecomposition and its Applications
Journal of VLSI Signal Processing Systems
Letters: Fully complex extreme learning machine
Neurocomputing
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Robust recursive complex extreme learning machine algorithm for finite numerical precision
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Line search algorithms for adaptive filtering
IEEE Transactions on Signal Processing
The Data Least Squares Problem and Channel Equalization
IEEE Transactions on Signal Processing
Channel equalization using adaptive complex radial basis function networks
IEEE Journal on Selected Areas in Communications
Universal approximation using incremental constructive feedforward networks with random hidden nodes
IEEE Transactions on Neural Networks
A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks
IEEE Transactions on Neural Networks
Hi-index | 0.01 |
Recently, a new learning algorithm for a single-hidden-layer feedforward neural network (SLFN), named the complex extreme learning machine (C-ELM), has been proposed in Li et al. [Fully complex extreme learning machine, Neurocomputing 68 (2005) 306-314]. Although it shows potential applicability in many areas, there is still room for improvement in performance, especially in training-based equalization applications in which the noise is only within the received data. In this paper, we propose a new solution applying the data least squares (DLS) method. Simulations show that DLS-based C-ELM outperforms the ordinary-least-square-based one in channel equalization problems.