The application of neural networks to communication channel equalisation : a comparison between localised and non-localised basis functions

 

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dc.contributor.advisor Greene, John en_ZA
dc.contributor.author Olshewsky, Avron Bernard en_ZA
dc.date.accessioned 2014-11-10T08:54:54Z
dc.date.available 2014-11-10T08:54:54Z
dc.date.issued 1997 en_ZA
dc.identifier.citation Olshewsky, A. 1997. The application of neural networks to communication channel equalisation : a comparison between localised and non-localised basis functions. University of Cape Town. en_ZA
dc.identifier.uri http://hdl.handle.net/11427/9472
dc.description Bibliography: leaves. 63-66. en_ZA
dc.description.abstract Neural networks have been applied to a number of problems over the past few years. One of the emerging applications of neural networks is adaptive communication channel equalisation. This area of research has become prominent due to the reformulation of the equalisation problem as a classification problem. Viewing equalisation as a classification problem allows researchers to apply the knowledge gained from other fields to equalisation. A wide variety of neural network structures have been suggested to equalise communication channels. Each structure may in turn have a number of different possible algorithms to train the equaliser. A neural network is essentially a non-linear classifier; in general a neural network is able to classify data by employing a non-linear function. The primary subject of this dissertation is the comparative performance of neural networks employing non-localised basis (non-linear) functions (Multi-layer Perceptron) versus those employing localised basis functions (Radial Basis Function Network). en_ZA
dc.language.iso eng en_ZA
dc.subject.other Electrical Engineering en_ZA
dc.title The application of neural networks to communication channel equalisation : a comparison between localised and non-localised basis functions en_ZA
dc.type Master Thesis
uct.type.publication Research en_ZA
uct.type.resource Thesis en_ZA
dc.publisher.institution University of Cape Town
dc.publisher.faculty Faculty of Engineering and the Built Environment
dc.publisher.department Department of Electrical Engineering en_ZA
dc.type.qualificationlevel Masters
dc.type.qualificationname MSc en_ZA
uct.type.filetype Text
uct.type.filetype Image
dc.identifier.apacitation Olshewsky, A. B. (1997). <i>The application of neural networks to communication channel equalisation : a comparison between localised and non-localised basis functions</i>. (Thesis). University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering. Retrieved from http://hdl.handle.net/11427/9472 en_ZA
dc.identifier.chicagocitation Olshewsky, Avron Bernard. <i>"The application of neural networks to communication channel equalisation : a comparison between localised and non-localised basis functions."</i> Thesis., University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 1997. http://hdl.handle.net/11427/9472 en_ZA
dc.identifier.vancouvercitation Olshewsky AB. The application of neural networks to communication channel equalisation : a comparison between localised and non-localised basis functions. [Thesis]. University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 1997 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/9472 en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Olshewsky, Avron Bernard AB - Neural networks have been applied to a number of problems over the past few years. One of the emerging applications of neural networks is adaptive communication channel equalisation. This area of research has become prominent due to the reformulation of the equalisation problem as a classification problem. Viewing equalisation as a classification problem allows researchers to apply the knowledge gained from other fields to equalisation. A wide variety of neural network structures have been suggested to equalise communication channels. Each structure may in turn have a number of different possible algorithms to train the equaliser. A neural network is essentially a non-linear classifier; in general a neural network is able to classify data by employing a non-linear function. The primary subject of this dissertation is the comparative performance of neural networks employing non-localised basis (non-linear) functions (Multi-layer Perceptron) versus those employing localised basis functions (Radial Basis Function Network). DA - 1997 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 1997 T1 - The application of neural networks to communication channel equalisation : a comparison between localised and non-localised basis functions TI - The application of neural networks to communication channel equalisation : a comparison between localised and non-localised basis functions UR - http://hdl.handle.net/11427/9472 ER - en_ZA


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