Automatic categorization of web text documents using fuzzy inference rule

Sadhana ◽  
2020 ◽  
Vol 45 (1) ◽  
Author(s):  
Ankita Dhar ◽  
Himadri Mukherjee ◽  
Niladri Sekhar Dash ◽  
Kaushik Roy
Keyword(s):  
Fuzzy Inference ◽  
Inference Rule ◽  
Text Documents ◽  
Automatic Categorization

SIMPLIFIED FUZZY INFERENCE RULE-BASED GENETICALLY OPTIMIZED HYBRID FUZZY NEURAL NETWORKS

2008 ◽  
Vol 16 (02) ◽  
pp. 245-274 ◽  
Author(s):  
BYOUNG-JUN PARK ◽  
WITOLD PEDRYCZ ◽  
SUNG-KWUN OH
Keyword(s):  
Neural Networks ◽  
Fuzzy Inference ◽  
Inference Rule ◽  
Least Square Method ◽  
Fuzzy Neural Networks ◽  
Least Square ◽  
Rule Based ◽  
Fuzzy Partition ◽  
Fuzzy Neural ◽  
Input Variables

In this study, we introduce an advanced architecture of genetically optimized Hybrid Fuzzy Neural Networks (gHFNN) and develop a comprehensive design methodology supporting their construction. A series of numeric experiments is included to illustrate the performance of the networks. The construction of gHFNN exploits fundamental technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms (GAs). The architecture of the gHFNNs results from a synergistic usage of the genetic optimization-driven hybrid system generated by combining Fuzzy Neural Networks (FNN) with Polynomial Neural Networks (PNN). In this tandem, a FNN supports the formation of the condition part of the rule-based structure of the gHFNN. The conclusion part of the gHFNN is designed using PNNs. We distinguish between two types of the simplified fuzzy inference rule-based FNN structures showing how this taxonomy depends upon the type of a fuzzy partition of input variables. As to the conclusion part of the gHFNN, the development of the PNN dwells on two general optimization mechanisms: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the gHFNN, we experimented with three representative numerical examples. A comparative analysis demonstrates that the proposed gHFNN come with higher accuracy as well as superb predictive capabilities when compared with other neurofuzzy models.

scholarly journals Automatic Categorization of Documents Using Latent Semantic Analysis and Fuzzy Inference Algorithm of Mamdani

2015 ◽  
Vol 1 (44) ◽  
pp. 5 ◽  
Author(s):  
Anatoly Dmitrievich Khomonenko ◽  
Sergej Vjacheslavovich Logashev ◽  
Sergey Aleksandrovich Krasnov
Keyword(s):  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Fuzzy Inference ◽  
Inference Algorithm ◽  
Automatic Categorization

scholarly journals Simulasi Kinerja Siswa Dengan Metode Fuzzy Inference Sugeno Menggunakan Aplikasi Matlab

2017 ◽  
Vol 11 (1) ◽  
pp. 71 ◽  
Author(s):  
Halimahtus Mukminna ◽  
Devita Maulina Putri ◽  
Anik Nur Handayani
Keyword(s):  
Fuzzy Inference ◽  
Inference Rule ◽  
Inference System ◽  
Artifical Intelligence

Tujuan artikel ini adalah membuat simulasi untuk penilaian kinerja siswa menggunakan logika fuzzy untuk mengatasi masalah proses penilaian evaluasi siswa. Disamping itu belum adanya sistem khusus yang dapat mengoptimalkan dalam memberikan dukungan bagi guru dalam melakukan evaluasi yang masih bersifat perhitungan manual. Satu cara penentuan perhitungan hasil evaluasi siswa dapat dipermudah dengan menggunakan bantuan pertimbangan Artifical Intelligence (AI) sebagai optimasinya. Dalam pertimbangan evaluasi kinerja siswa ini menggunakan logika fuzzy dengan metode inference system sugeno. Metode sugeno ini merupakan metode inference fuzzy untuk aturan yang direpresentasikan dalam bentuk IF-THEN, dimana output sistem tidak berupa himpunan fuzzy, melainkan berupa persamaan linier. Kriteria yang digunakan dalam penilaian kinerja siswa meliputi very unsuccesusful, unsuccessful, average, successful, dan very successful.  Pada simulasi ini hasil yang ditampilkan dengan perhitungan manual dan perhitungan Matlab sebagai pembandingnya hasil perhitungan secara manual nilai result 45,5 sedangkan pada perhitungan matlab nilai result sebesar 48,5. Sehingga dapat disimpulkan selisih yang disebabkan tingkat akurasi hasil inference rule pada perhitungan manual kurang efektif bahkan terkadang banyak inference rule yang harus disesuaikan.

scholarly journals Sistem Cerdas untuk Inovasi Blender Control System Menggunakan Fuzzy Control System dengan Metode Mamdani

2018 ◽  
pp. 115
Author(s):  
Khomarudin Fahuzan ◽  
Uke Ralmugiz
Keyword(s):  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Inference ◽  
Inference Rule ◽  
Rule Base ◽  
Inference Control ◽  
Fuzzy Control System ◽  
Hardness Level ◽  
A Value ◽  
Time Required

This research aims to establish a control system on blender by using fuzzy control system with mamdani method. In this study, researchers used input in the form of hardness level and volume of fruit to be blend, while the output is blend time (0 to 180 seconds) with assumption of constant blender velocity). Researchers used fuzzy inference control system with Mamdani method with some stages: fuzzification, inference, rule base, and defuzzification. Fuzzification changes the hardness of the fruit and the volume into a value. Inference created fuzzy output using pre-made rules. Defuzzification counted the time it takes to blend into output. Based on the results of the research, the results obtained for the sample of fruit with a level of hardness of 40%, and volume 4 (400 ml), in obtaining the minimum time required to smooth the fruit about 79 seconds. Thus the fuzzy control system can be used as an innovation to make the control system in blender. This system not only applies to blenders only, but also can be applied to other machines using fuzzy control system.

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