Using Fuzzy Feed-Forward Neural Network for Linguistic Processing

Fuzzy sets have been implemented efficiently to manage unclear data, language terms, and vague notions. Recently, considerable work has been dedicated to merging neural-network techniques with fuzzy sets. In this study, present the structure of a fuzzy feed-forward neural network (FFFNN) with a tra...

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Main Authors: AbdulRahim K. Rah, Sozan S. Haydar
Format: Article
Language:English
Published: College of science, university of Diyala 2023-04-01
Series:Academic Science Journal
Online Access:https://acadscij.uodiyala.edu.iq/index.php/Home/article/view/115
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author AbdulRahim K. Rah
Sozan S. Haydar
author_facet AbdulRahim K. Rah
Sozan S. Haydar
author_sort AbdulRahim K. Rah
collection DOAJ
description Fuzzy sets have been implemented efficiently to manage unclear data, language terms, and vague notions. Recently, considerable work has been dedicated to merging neural-network techniques with fuzzy sets. In this study, present the structure of a fuzzy feed-forward neural network (FFFNN) with a trapezoidal fuzzy set. In addition to handling real input vectors, it is also capable of handling fuzzy input vectors. Generally, the output of a FNN is a fuzzy vector. According to the extension principle of Zadeh, each unit of a FNN has an input-output relationship. To determine the costs associated with fuzzy calculations and fuzzy objectives, developed a cost function. At that point, created a learning algorithm from the cost capacity to align the four variables of each trapezoidal fuzzy weight. In conclusion, demonstrate our methodology using numerical models.
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institution Kabale University
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language English
publishDate 2023-04-01
publisher College of science, university of Diyala
record_format Article
series Academic Science Journal
spelling doaj-art-22ece77a94da4706bc2295979c96042f2024-11-19T10:02:38ZengCollege of science, university of DiyalaAcademic Science Journal2958-46122959-55682023-04-011210.24237/ASJ.01.02.723BUsing Fuzzy Feed-Forward Neural Network for Linguistic ProcessingAbdulRahim K. RahSozan S. Haydar Fuzzy sets have been implemented efficiently to manage unclear data, language terms, and vague notions. Recently, considerable work has been dedicated to merging neural-network techniques with fuzzy sets. In this study, present the structure of a fuzzy feed-forward neural network (FFFNN) with a trapezoidal fuzzy set. In addition to handling real input vectors, it is also capable of handling fuzzy input vectors. Generally, the output of a FNN is a fuzzy vector. According to the extension principle of Zadeh, each unit of a FNN has an input-output relationship. To determine the costs associated with fuzzy calculations and fuzzy objectives, developed a cost function. At that point, created a learning algorithm from the cost capacity to align the four variables of each trapezoidal fuzzy weight. In conclusion, demonstrate our methodology using numerical models. https://acadscij.uodiyala.edu.iq/index.php/Home/article/view/115
spellingShingle AbdulRahim K. Rah
Sozan S. Haydar
Using Fuzzy Feed-Forward Neural Network for Linguistic Processing
Academic Science Journal
title Using Fuzzy Feed-Forward Neural Network for Linguistic Processing
title_full Using Fuzzy Feed-Forward Neural Network for Linguistic Processing
title_fullStr Using Fuzzy Feed-Forward Neural Network for Linguistic Processing
title_full_unstemmed Using Fuzzy Feed-Forward Neural Network for Linguistic Processing
title_short Using Fuzzy Feed-Forward Neural Network for Linguistic Processing
title_sort using fuzzy feed forward neural network for linguistic processing
url https://acadscij.uodiyala.edu.iq/index.php/Home/article/view/115
work_keys_str_mv AT abdulrahimkrah usingfuzzyfeedforwardneuralnetworkforlinguisticprocessing
AT sozanshaydar usingfuzzyfeedforwardneuralnetworkforlinguisticprocessing