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Toward utilization of neuro-fuzzy sy...

Southern Illinois University at Carbondale., Computer Science.

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Toward utilization of neuro-fuzzy systems for taxonomic identification using psittacines as a case study.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Toward utilization of neuro-fuzzy systems for taxonomic identification using psittacines as a case study./
作者:	Spiess, Cynthia R.
面頁冊數:	71 p.
附註:	Adviser: Shahram Rahimi.
Contained By:	Masters Abstracts International47-01.
標題:	Artificial Intelligence. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1456955
ISBN:	9780549734017

Toward utilization of neuro-fuzzy systems for taxonomic identification using psittacines as a case study.
Spiess, Cynthia R.

Toward utilization of neuro-fuzzy systems for taxonomic identification using psittacines as a case study. - 71 p.

Adviser: Shahram Rahimi.

Thesis (M.S.)--Southern Illinois University at Carbondale, 2008.

Demonstration of the neuro-fuzzy application NEFCLASS-J applied to the task of psittacine (parrot) taxonomic identification is presented in this thesis. Backgrounds on fuzzy logic and artificial neural networks along with methods to increase system accuracy are discussed. Neuro-fuzzy techniques are reviewed. The neuro-fuzzy systems ANFIS, GARIC, FUN, and SONFIN are briefly analyzed, followed by a more in-depth coverage of NEFCLASS-J. NEFCLASS-J is applied to parrot data for 141 and 183 groupings using 68 feature points or qualities. Results showed classification accuracies above 95% correct, which seem strongly tied to certain neuro-fuzzy system parameter values. Rule base sizes were in the range of (1,750, 1,950) rules. Better systems can probably be made with the help of experts from all the fields collaborating.

ISBN: 9780549734017Subjects--Topical Terms:

769149
Artificial Intelligence.

Toward utilization of neuro-fuzzy systems for taxonomic identification using psittacines as a case study.
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520 $a Demonstration of the neuro-fuzzy application NEFCLASS-J applied to the task of psittacine (parrot) taxonomic identification is presented in this thesis. Backgrounds on fuzzy logic and artificial neural networks along with methods to increase system accuracy are discussed. Neuro-fuzzy techniques are reviewed. The neuro-fuzzy systems ANFIS, GARIC, FUN, and SONFIN are briefly analyzed, followed by a more in-depth coverage of NEFCLASS-J. NEFCLASS-J is applied to parrot data for 141 and 183 groupings using 68 feature points or qualities. Results showed classification accuracies above 95% correct, which seem strongly tied to certain neuro-fuzzy system parameter values. Rule base sizes were in the range of (1,750, 1,950) rules. Better systems can probably be made with the help of experts from all the fields collaborating.
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