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Genetic and learning automata algorithms for adaptive digital filters
Two different approaches to adaptive digital filtering based on learning algorithms are presented in detail. The first approach is based on stochastic learning automata where the discretized values of a parameter(s) form the actions of a learning automata which then obtains the optimal parameter set...
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| container_end_page | 44 vol.4 |
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| container_start_page | 41 |
| container_title | |
| container_volume | 4 |
| creator | Nambiar, R. Tang, C.K.K. Mars, P. |
| description | Two different approaches to adaptive digital filtering based on learning algorithms are presented in detail. The first approach is based on stochastic learning automata where the discretized values of a parameter(s) form the actions of a learning automata which then obtains the optimal parameter setting using a suitably defined error function as the feedback from the environment. The authors detail the use of improved learning schemes published elsewhere and also point out the basic shortcoming of this approach. The second approach is based on genetic algorithms (GAs). GAs have been used in the context of multiparameter optimization. Simulation results are presented to show how this approach is able to tackle the problems of dimensionality when adapting high-order filters. The effect of the differential parameters of a GA on the learning process is also demonstrated. Comparative results between a pure random search algorithm and the GA are also presented.< > |
| doi_str_mv | 10.1109/ICASSP.1992.226416 |
| format | conference_proceeding |
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The first approach is based on stochastic learning automata where the discretized values of a parameter(s) form the actions of a learning automata which then obtains the optimal parameter setting using a suitably defined error function as the feedback from the environment. The authors detail the use of improved learning schemes published elsewhere and also point out the basic shortcoming of this approach. The second approach is based on genetic algorithms (GAs). GAs have been used in the context of multiparameter optimization. Simulation results are presented to show how this approach is able to tackle the problems of dimensionality when adapting high-order filters. The effect of the differential parameters of a GA on the learning process is also demonstrated. 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The first approach is based on stochastic learning automata where the discretized values of a parameter(s) form the actions of a learning automata which then obtains the optimal parameter setting using a suitably defined error function as the feedback from the environment. The authors detail the use of improved learning schemes published elsewhere and also point out the basic shortcoming of this approach. The second approach is based on genetic algorithms (GAs). GAs have been used in the context of multiparameter optimization. Simulation results are presented to show how this approach is able to tackle the problems of dimensionality when adapting high-order filters. The effect of the differential parameters of a GA on the learning process is also demonstrated. Comparative results between a pure random search algorithm and the GA are also presented.< ></description><subject>Adaptive filters</subject><subject>Digital filters</subject><subject>Filtering algorithms</subject><subject>Finite impulse response filter</subject><subject>Genetics</subject><subject>IIR filters</subject><subject>Learning automata</subject><subject>Least squares methods</subject><subject>Stability</subject><subject>Stochastic processes</subject><issn>1520-6149</issn><issn>2379-190X</issn><isbn>9780780305328</isbn><isbn>0780305329</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1992</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotUNtKw0AUXLyAoeYH-rQ_kLj3zT5KqVUoKFTBt3KSnI0ruZTNKvj3BtphYOZpZhhC1pyVnDP38LJ5PBzeSu6cKIUwipsrkglpXcEd-7wmubMVWyiZlqK6IRnXghWGK3dH8nn-ZguU5laJjGx3OGIKDYWxpT1CHMPYUfhJ0wAJKPTdFEP6Gmbqp0ihhVMKv0jb0IUEPfWhTxjne3LroZ8xv-iKfDxt3zfPxf51t6zdF2FpSwX3oATDGrS0dVWhQdZ4bZtK1dBo4YXlvGK61s6ilL5Gox2gEs5ou7hGrsj6nBsQ8XiKYYD4dzx_IP8B4eROjQ</recordid><startdate>1992</startdate><enddate>1992</enddate><creator>Nambiar, R.</creator><creator>Tang, C.K.K.</creator><creator>Mars, P.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>1992</creationdate><title>Genetic and learning automata algorithms for adaptive digital filters</title><author>Nambiar, R. ; Tang, C.K.K. ; Mars, P.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i174t-1fa420eba537b88e6e0cf57c84bac52f2711805b597e33fbe659ae42965759ac3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1992</creationdate><topic>Adaptive filters</topic><topic>Digital filters</topic><topic>Filtering algorithms</topic><topic>Finite impulse response filter</topic><topic>Genetics</topic><topic>IIR filters</topic><topic>Learning automata</topic><topic>Least squares methods</topic><topic>Stability</topic><topic>Stochastic processes</topic><toplevel>online_resources</toplevel><creatorcontrib>Nambiar, R.</creatorcontrib><creatorcontrib>Tang, C.K.K.</creatorcontrib><creatorcontrib>Mars, P.</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 Xplore Digital 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>Nambiar, R.</au><au>Tang, C.K.K.</au><au>Mars, P.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Genetic and learning automata algorithms for adaptive digital filters</atitle><btitle>[Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing</btitle><stitle>ICASSP</stitle><date>1992</date><risdate>1992</risdate><volume>4</volume><spage>41</spage><epage>44 vol.4</epage><pages>41-44 vol.4</pages><issn>1520-6149</issn><eissn>2379-190X</eissn><isbn>9780780305328</isbn><isbn>0780305329</isbn><abstract>Two different approaches to adaptive digital filtering based on learning algorithms are presented in detail. The first approach is based on stochastic learning automata where the discretized values of a parameter(s) form the actions of a learning automata which then obtains the optimal parameter setting using a suitably defined error function as the feedback from the environment. The authors detail the use of improved learning schemes published elsewhere and also point out the basic shortcoming of this approach. The second approach is based on genetic algorithms (GAs). GAs have been used in the context of multiparameter optimization. Simulation results are presented to show how this approach is able to tackle the problems of dimensionality when adapting high-order filters. The effect of the differential parameters of a GA on the learning process is also demonstrated. Comparative results between a pure random search algorithm and the GA are also presented.< ></abstract><pub>IEEE</pub><doi>10.1109/ICASSP.1992.226416</doi></addata></record> |
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| identifier | ISSN: 1520-6149 |
| ispartof | [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, 1992, Vol.4, p.41-44 vol.4 |
| issn | 1520-6149 2379-190X |
| language | eng |
| recordid | cdi_ieee_primary_226416 |
| source | IEEE Xplore All Conference Series |
| subjects | Adaptive filters Digital filters Filtering algorithms Finite impulse response filter Genetics IIR filters Learning automata Least squares methods Stability Stochastic processes |
| title | Genetic and learning automata algorithms for adaptive digital filters |
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