A new neural network based sequence estimator in non-Gaussian noise environment
The application of neural network for sequence estimation in the presence of both impulsive noise and intersymbol interference is presented. In this estimator, a nonlinearity is embedded in the conventional steepest descent method for suppressing the impulse noise during the iteration and thus a dua...
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| container_end_page | 1587 vol.3 |
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| creator | Weng, J.F. Leung, S.H. Bi, G.G. |
| description | The application of neural network for sequence estimation in the presence of both impulsive noise and intersymbol interference is presented. In this estimator, a nonlinearity is embedded in the conventional steepest descent method for suppressing the impulse noise during the iteration and thus a dual nonlinear steepest descent algorithm is developed for estimating the symbol sequence. This algorithm can be implemented by a recurrent correlation neural network with highly parallel processing. To further improve the performance, a decision feedback technique is developed. It is shown in computer simulations that the new estimator outperforms the linear Viterbi algorithm particularly when there is impulse noise. |
| doi_str_mv | 10.1109/ICNN.1996.549136 |
| format | Conference Proceeding |
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In this estimator, a nonlinearity is embedded in the conventional steepest descent method for suppressing the impulse noise during the iteration and thus a dual nonlinear steepest descent algorithm is developed for estimating the symbol sequence. This algorithm can be implemented by a recurrent correlation neural network with highly parallel processing. To further improve the performance, a decision feedback technique is developed. It is shown in computer simulations that the new estimator outperforms the linear Viterbi algorithm particularly when there is impulse noise.</description><identifier>ISBN: 0780332105</identifier><identifier>ISBN: 9780780332102</identifier><identifier>DOI: 10.1109/ICNN.1996.549136</identifier><language>eng</language><publisher>IEEE</publisher><subject>Additive noise ; Gaussian noise ; Hopfield neural networks ; Intelligent networks ; Interference ; Neural networks ; Noise shaping ; Recurrent neural networks ; Viterbi algorithm ; Working environment noise</subject><ispartof>Proceedings of International Conference on Neural Networks (ICNN'96), 1996, Vol.3, p.1582-1587 vol.3</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/549136$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,777,781,786,787,2052,4036,4037,27906,54901</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/549136$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Weng, J.F.</creatorcontrib><creatorcontrib>Leung, S.H.</creatorcontrib><creatorcontrib>Bi, G.G.</creatorcontrib><title>A new neural network based sequence estimator in non-Gaussian noise environment</title><title>Proceedings of International Conference on Neural Networks (ICNN'96)</title><addtitle>ICNN</addtitle><description>The application of neural network for sequence estimation in the presence of both impulsive noise and intersymbol interference is presented. In this estimator, a nonlinearity is embedded in the conventional steepest descent method for suppressing the impulse noise during the iteration and thus a dual nonlinear steepest descent algorithm is developed for estimating the symbol sequence. This algorithm can be implemented by a recurrent correlation neural network with highly parallel processing. To further improve the performance, a decision feedback technique is developed. It is shown in computer simulations that the new estimator outperforms the linear Viterbi algorithm particularly when there is impulse noise.</description><subject>Additive noise</subject><subject>Gaussian noise</subject><subject>Hopfield neural networks</subject><subject>Intelligent networks</subject><subject>Interference</subject><subject>Neural networks</subject><subject>Noise shaping</subject><subject>Recurrent neural networks</subject><subject>Viterbi algorithm</subject><subject>Working environment noise</subject><isbn>0780332105</isbn><isbn>9780780332102</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1996</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNp9jssKwjAURAMi-OpeXOUHWpM-zVKKr03duC9RrxBtE81tLf69EV07MAzDgWEImXIWcM7EfJcXRcCFSIMkFjxKe2TEsgWLopCzZEA8xCtzipMkzLIh2S-phs65tbJy0XTG3uhRIpwpwqMFfQIK2KhaNsZSpak22t_IFlHJT1HouH4qa3QNupmQ_kVWCN4vx2S2Xh3yra8AoLxbt2Nf5fda9Be-AQ4WPiw</recordid><startdate>1996</startdate><enddate>1996</enddate><creator>Weng, J.F.</creator><creator>Leung, S.H.</creator><creator>Bi, G.G.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>1996</creationdate><title>A new neural network based sequence estimator in non-Gaussian noise environment</title><author>Weng, J.F. ; Leung, S.H. ; Bi, G.G.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-ieee_primary_5491363</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1996</creationdate><topic>Additive noise</topic><topic>Gaussian noise</topic><topic>Hopfield neural networks</topic><topic>Intelligent networks</topic><topic>Interference</topic><topic>Neural networks</topic><topic>Noise shaping</topic><topic>Recurrent neural networks</topic><topic>Viterbi algorithm</topic><topic>Working environment noise</topic><toplevel>online_resources</toplevel><creatorcontrib>Weng, J.F.</creatorcontrib><creatorcontrib>Leung, S.H.</creatorcontrib><creatorcontrib>Bi, G.G.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE/IET Electronic Library</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Weng, J.F.</au><au>Leung, S.H.</au><au>Bi, G.G.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A new neural network based sequence estimator in non-Gaussian noise environment</atitle><btitle>Proceedings of International Conference on Neural Networks (ICNN'96)</btitle><stitle>ICNN</stitle><date>1996</date><risdate>1996</risdate><volume>3</volume><spage>1582</spage><epage>1587 vol.3</epage><pages>1582-1587 vol.3</pages><isbn>0780332105</isbn><isbn>9780780332102</isbn><abstract>The application of neural network for sequence estimation in the presence of both impulsive noise and intersymbol interference is presented. In this estimator, a nonlinearity is embedded in the conventional steepest descent method for suppressing the impulse noise during the iteration and thus a dual nonlinear steepest descent algorithm is developed for estimating the symbol sequence. This algorithm can be implemented by a recurrent correlation neural network with highly parallel processing. To further improve the performance, a decision feedback technique is developed. It is shown in computer simulations that the new estimator outperforms the linear Viterbi algorithm particularly when there is impulse noise.</abstract><pub>IEEE</pub><doi>10.1109/ICNN.1996.549136</doi></addata></record> |
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| identifier | ISBN: 0780332105 |
| ispartof | Proceedings of International Conference on Neural Networks (ICNN'96), 1996, Vol.3, p.1582-1587 vol.3 |
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| language | eng |
| recordid | cdi_ieee_primary_549136 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Additive noise Gaussian noise Hopfield neural networks Intelligent networks Interference Neural networks Noise shaping Recurrent neural networks Viterbi algorithm Working environment noise |
| title | A new neural network based sequence estimator in non-Gaussian noise environment |
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