A functional link artificial neural network for adaptive channel equalization

Patra, J.C.; Pal, Rajarshi

doi:10.1016/0165-1684(94)00152-p

Cited by 192 publications

(88 citation statements)

References 7 publications

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“…Functional Link Neural Network is a class of HONNs created by Pao [7] and has been successfully used in many applications such as system identification [9][10][11][12][13][14], channel equalization [3], classification [15][16][17][18], pattern recognition [19,20] and prediction [21,22]. In this paper, we would discuss on the FLNN for the prediction task.…”

Section: Functional Link Neural Networkmentioning

confidence: 99%

“…The most common architecture of ANNs is the Multi-layer feed forward network known as Multilayer perceptron (MLP). Since the MLP has multilayered structure, the network requires excessive training time for learning [3]. This is because, the number of weight and the training time will increase as the number of layers and the nodes in layer increases [3,4].…”

Section: Introductionmentioning

confidence: 99%

“…Since the MLP has multilayered structure, the network requires excessive training time for learning [3]. This is because, the number of weight and the training time will increase as the number of layers and the nodes in layer increases [3,4]. In order to overcome the drawback of MLP, another type of network known as Higher Order Neural Networks (HONNs) have been introduced [5].…”

Section: Introductionmentioning

confidence: 99%

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Functional Link Neural Network – Artificial Bee Colony for Time Series Temperature Prediction

Hassim

Ghazali

2013

Lecture Notes in Computer Science

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Abstract. Higher Order Neural Networks (HONNs) have emerged as an important tool for time series prediction and have been successfully applied in many engineering and scientific problems. One of the models in HONNs is a Functional Link Neural Network (FLNN) known to be conveniently used for function approximation and can be extended for pattern recognition with faster convergence rate and lesser computational load compared to ordinary feedforward network like the Multilayer Perceptron (MLP). In training the FLNN, the mostly used algorithm is the Backpropagation (BP) learning algorithm. However, one of the crucial problems with BP learning algorithm is that it can be easily gets trapped on local minima. This paper proposed an alternative learning scheme for the FLNN to be applied on temperature forecasting by using Artificial Bee Colony (ABC) optimization algorithm. The ABC adopted in this work is known to have good exploration and exploitation capabilities in searching optimal weight especially in numerical optimization problems. The result of the prediction made by FLNN-ABC is compared with the original FLNN architecture and toward the end we found that FLNN-ABC gives better result in predicting the next-day ahead prediction.

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Section: Functional Link Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Functional Link Neural Network – Artificial Bee Colony for Time Series Temperature Prediction

Hassim

Ghazali

2013

Lecture Notes in Computer Science

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“…The free parameters (weight and bias) of the ANN are adjusted according to the channel condition by transmitting a training sequence. MLP as well as the radial basis function network and the recurrent ANN have been proposed and applied for channel equalization, details of which can be found in [20,23,24,34,36] and references therein. This study is limited to the equalization using the feedforward MLP with BP training algorithm.…”

Section: Artificial Neural Network Adaptive Equalizermentioning

confidence: 99%

“…Fundamentally, the problem of adaptive equalization can be formulated as a classification problem [24,34], and modern classifying tools like ANN can be utilised. ANN is more suitable for channel equalization because of highly parallel structure, adaptability and learning capability.…”

Section: Artificial Neural Network Adaptive Equalizermentioning

confidence: 99%

Bit error performance of diffuse indoor optical wireless channel pulse position modulation system employing artificial neural networks for channel equalisation

Rajbhandari

Ghassemlooy

Angelova

2009

IET Optoelectronics

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Citation: Rajbhandari, Sujan, Ghassemlooy, Zabih and Angelova, Maia (2009) Bit error performance of diffuse indoor optical wireless channel pulse position modulation system employing artificial neural networks for channel equalisation. IET Optoelectronics, 3 (4 Northumbria University has developed Northumbria Research Link (NRL) to enable users to access the University's research output. Copyright © and moral rights for items on NRL are retained by the individual author(s) and/or other copyright owners. Single copies of full items can be reproduced, displayed or performed, and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided the authors, title and full bibliographic details are given, as well as a hyperlink and/or URL to the original metadata page. The content must not be changed in any way. Full items must not be sold commercially in any format or medium without formal permission of the copyright holder. The full policy is available online: http://nrl.northumbria.ac.uk/policies.html This document may differ from the final, published version of the research and has been made available online in accordance with publisher policies. To read and/or cite from the published version of the research, please visit the publisher's website (a subscription may be required.)The AbstractThe bit error rate (BER) performance of a pulse position modulation (PPM) scheme for non line-of-sight (LOS) indoor optical links employing channel equalization based on the artificial neural network (ANN) is reported in the paper. Channel equalization is achieved by training a multilayer perceptrons (MLP) ANN. A comparative study of the unequalized "soft" decision decoding and the "hard" decision decoding along with the neural equalized "soft" decision decoding is presented for different bit resolutions for optical channels with different delay spread. We show that the unequalized "hard" decision decoding performs the worst for all values of normalized delayed spread becoming impractical beyond normalized delayed spread of 0.6. However, "soft" decision decoding with/without equalization displays relatively improved performance for all values of the delay spread. The study shows that for a highly diffuse channel the signal-to-noise ratio (SNR) requirement to achieve a BER of 10 -5 for the ANN based equalizer is ~ 10 dB lower compared with the unequalized "soft" decoding for 16-PPM at a data rate of 155 Mbps. Our results indicate that for all range of delay spread, neural network equalization is an effective tool of mitigating the ISI.

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Machine Learning for Joint Channel Equalization and Signal Detection

Zhang

Yang

2019

Machine Learning for Future Wireless Communications

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A functional link artificial neural network for adaptive channel equalization

Cited by 192 publications

References 7 publications

Functional Link Neural Network – Artificial Bee Colony for Time Series Temperature Prediction

Functional Link Neural Network – Artificial Bee Colony for Time Series Temperature Prediction

Bit error performance of diffuse indoor optical wireless channel pulse position modulation system employing artificial neural networks for channel equalisation

Machine Learning for Joint Channel Equalization and Signal Detection

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