Fuzzy Neural Network Approach for Estimating The K-distribution Parameters
This paper provides a novel approach based on neuro-fuzzy inference system for the estimation problem of the K-distributed parameters. The proposed method is based on a network implementation with real weights and the genetic algorithm (GA) tool is applied for an off-line training of the fuzzy-neura...
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| creator | Mezache, A. Soltani, F. |
| description | This paper provides a novel approach based on neuro-fuzzy inference system for the estimation problem of the K-distributed parameters. The proposed method is based on a network implementation with real weights and the genetic algorithm (GA) tool is applied for an off-line training of the fuzzy-neural network (FNN) shape parameter estimator. Moreover, the proposed estimator combines the Raghavan's and ML/MOM (maximum-likelihood and moments) methods and the experimental results are presented to demonstrate the validity of the approach. It is shown that such the FNN estimator is successful with a lower variance of parameter estimates when compared with existing Raghavan's and ML/MOM approaches. |
| doi_str_mv | 10.1109/ICSPC.2007.4728574 |
| format | Conference Proceeding |
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The proposed method is based on a network implementation with real weights and the genetic algorithm (GA) tool is applied for an off-line training of the fuzzy-neural network (FNN) shape parameter estimator. Moreover, the proposed estimator combines the Raghavan's and ML/MOM (maximum-likelihood and moments) methods and the experimental results are presented to demonstrate the validity of the approach. 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The proposed method is based on a network implementation with real weights and the genetic algorithm (GA) tool is applied for an off-line training of the fuzzy-neural network (FNN) shape parameter estimator. Moreover, the proposed estimator combines the Raghavan's and ML/MOM (maximum-likelihood and moments) methods and the experimental results are presented to demonstrate the validity of the approach. It is shown that such the FNN estimator is successful with a lower variance of parameter estimates when compared with existing Raghavan's and ML/MOM approaches.</description><subject>Clutter</subject><subject>Fuzzy control</subject><subject>Fuzzy neural networks</subject><subject>Genetic algorithms</subject><subject>K-distribution</subject><subject>Maximum likelihood estimation</subject><subject>Message-oriented middleware</subject><subject>Parameter estimation</subject><subject>Shape</subject><subject>shape parameter</subject><subject>Signal processing</subject><subject>Statistics</subject><isbn>9781424412358</isbn><isbn>1424412358</isbn><isbn>9781424412365</isbn><isbn>1424412366</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2007</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVkNFKwzAYhSMycMy-gN7kBVrzp0naXo6y6XTowHk90uSvi25rSVNke_pV3I3n5uPA4XA4hNwBSwBY8bAo31dlwhnLEpHxXGbiikRFloPgQgBPlbz-52U-IuPfeMElwA2Juu6LDRJSgFBj8jzvT6cjfcXe692A8NP4bzptW99os6V14-msC26vgzt80vUW6UtsXRe8q_rgmgNdaa_3GNB3t2RU612H0YUT8jGfrcunePn2uCiny9hxKEKspLbWGG44Mo0AKQeRa6UUGExZwSptM4mmriAFY3SFmbK1ZgYsrw1Km07I_V-vQ8RN64dx_ri5vJGeAcuPUnA</recordid><startdate>20070101</startdate><enddate>20070101</enddate><creator>Mezache, A.</creator><creator>Soltani, F.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>20070101</creationdate><title>Fuzzy Neural Network Approach for Estimating The K-distribution Parameters</title><author>Mezache, A. ; Soltani, F.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i219t-65addcc2c2e0ae1132148a6661ce3090bad75ecfb131ccabe76dfa0c1d2fce5d3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Clutter</topic><topic>Fuzzy control</topic><topic>Fuzzy neural networks</topic><topic>Genetic algorithms</topic><topic>K-distribution</topic><topic>Maximum likelihood estimation</topic><topic>Message-oriented middleware</topic><topic>Parameter estimation</topic><topic>Shape</topic><topic>shape parameter</topic><topic>Signal processing</topic><topic>Statistics</topic><toplevel>online_resources</toplevel><creatorcontrib>Mezache, A.</creatorcontrib><creatorcontrib>Soltani, F.</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 Electronic Library (IEL)</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>Mezache, A.</au><au>Soltani, F.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Fuzzy Neural Network Approach for Estimating The K-distribution Parameters</atitle><btitle>2007 IEEE International Conference on Signal Processing and Communications</btitle><stitle>ICSPC</stitle><date>2007-01-01</date><risdate>2007</risdate><spage>1335</spage><epage>1338</epage><pages>1335-1338</pages><isbn>9781424412358</isbn><isbn>1424412358</isbn><eisbn>9781424412365</eisbn><eisbn>1424412366</eisbn><abstract>This paper provides a novel approach based on neuro-fuzzy inference system for the estimation problem of the K-distributed parameters. The proposed method is based on a network implementation with real weights and the genetic algorithm (GA) tool is applied for an off-line training of the fuzzy-neural network (FNN) shape parameter estimator. Moreover, the proposed estimator combines the Raghavan's and ML/MOM (maximum-likelihood and moments) methods and the experimental results are presented to demonstrate the validity of the approach. It is shown that such the FNN estimator is successful with a lower variance of parameter estimates when compared with existing Raghavan's and ML/MOM approaches.</abstract><pub>IEEE</pub><doi>10.1109/ICSPC.2007.4728574</doi><tpages>4</tpages><oa>free_for_read</oa></addata></record> |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Clutter Fuzzy control Fuzzy neural networks Genetic algorithms K-distribution Maximum likelihood estimation Message-oriented middleware Parameter estimation Shape shape parameter Signal processing Statistics |
| title | Fuzzy Neural Network Approach for Estimating The K-distribution Parameters |
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