Journal ArticleDOI
An adaptive decision feedback equalizer based on the combination of the FIR and FLNN
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TLDR
A novel adaptive decision feedback equalizer with the combination of finite impulse response filter and functional link neural network is introduced, which results in improving the convergence speed while retaining the lower steady-state error at the cost of a small increasing computational burden.About:
This article is published in Digital Signal Processing.The article was published on 2011-12-01. It has received 29 citations till now. The article focuses on the topics: Adaptive equalizer & Finite impulse response.read more
Citations
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Journal ArticleDOI
Artificial Neural Network trained by Particle Swarm Optimization for non-linear channel equalization
TL;DR: Extensive simulations presented in this paper shows that, as compared to other ANN based equalizers as well as Neuro-fuzzy equalizers, the proposed equalizer performs better in all noise conditions.
Journal ArticleDOI
An Efficient JAYA Algorithm with Lévy Flight for Non-linear Channel Equalization
TL;DR: The results of experimental analyses conducted indicate that proposed JAYALF algorithm has a better exploration ability and converges quickly without getting trapped into local optima, and significantly outperforms JAYA, variants of JayA, state-of-the-art algorithms and BP algorithm in terms of solution quality, convergence speed, and robustness.
Journal ArticleDOI
Robust nonlinear channel equalization using WNN trained by symbiotic organism search algorithm
TL;DR: This paper develops equalizers based on a nonlinear neural structure (wavelet neural network (WNN) and train it's weighted by a recently developed meta-heuristic (symbiotic organisms search algorithm) and demonstrates the robustness in the performance of the proposed equalizers to handle the burst error conditions.
Journal ArticleDOI
A novel sign adaptation scheme for convex combination of two adaptive filters
TL;DR: In this article, a sign adaptation scheme for convex combination of adaptive filters is proposed to improve the performance and reduce the computational complexity of convex convex combinations of adaptive filter.
Journal ArticleDOI
Complex-valued pipelined decision feedback recurrent neural network for non-linear channel equalisation
TL;DR: Simulations demonstrate that the CPDFRNN equaliser using the amplitude CRTRL algorithm with less computational complexity not only eliminates the adverse effects of the nesting architecture, but also provides a superior performance over the CRNN and CDFRNNequalisers for non-linear channels in wireless communication systems.
References
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Book
Neural Networks: A Comprehensive Foundation
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Journal ArticleDOI
Learning representations by back-propagating errors
TL;DR: Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain.
Book
Adaptive Filter Theory
TL;DR: In this paper, the authors propose a recursive least square adaptive filter (RLF) based on the Kalman filter, which is used as the unifying base for RLS Filters.
Book
Adaptive pattern recognition and neural networks
TL;DR: This is a book that will show you even new to old thing, and when you are really dying of adaptive pattern recognition and neural networks, just pick this book; it will be right for you.
Journal ArticleDOI
A clustering technique for digital communications channel equalization using radial basis function networks
TL;DR: It is shown that the radial basis function network has an identical structure to the optimal Bayesian symbol-decision equalizer solution and, therefore, can be employed to implement the Bayesian equalizer.
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