scholarly journals Fuzzy Neural Network for Pattern Classification

2020 ◽  
Vol 167 ◽  
pp. 2606-2616 ◽  
Author(s):  
Arun Kulkarni ◽  
Nikita kulkarni
Keyword(s):  
Neural Network ◽  
Pattern Classification ◽  
Fuzzy Neural Network ◽  
Fuzzy Neural

scholarly journals Pruning Fuzzy Neural Network Applied to the Construction of Expert Systems to Aid in the Diagnosis of the Treatment of Cryotherapy and Immunotherapy

2019 ◽  
Vol 3 (2) ◽  
pp. 22 ◽  
Author(s):  
Augusto Junio Guimarães ◽  
Paulo Vitor de Campos Souza ◽  
Vinícius Jonathan Silva Araújo ◽  
Thiago Silva Rezende ◽  
Vanessa Souza Araújo
Keyword(s):  
Neural Network ◽  
Pattern Classification ◽  
Fuzzy Neural Network ◽  
Hybrid Approach ◽  
Hpv Infection ◽  
Current State ◽  
Fuzzy Neural ◽  
Classification Tasks ◽  
Intelligent Models ◽  
Excellent Tool

Human papillomavirus (HPV) infection is related to frequent cases of cervical cancer and genital condyloma in humans. Up to now, numerous methods have come into existence for the prevention and treatment of this disease. In this context, this paper aims to help predict the susceptibility of the patient to forms treatment using both cryotherapy and immunotherapy. These studies facilitate the choice of medications, which can be painful and embarrassing for patients who have warts on intimate parts. However, the use of intelligent models generates efficient results but does not allow a better interpretation of the results. To solve the problem, we present the method of a fuzzy neural network (FNN). A hybrid model capable of solving complex problems and extracting knowledge from the database will pruned through F-score techniques to perform pattern classification in the treatment of warts, and to produce a specialist system based on if/then rules, according to the experience obtained from the database collected through medical research. Finally, binary pattern-classification tests realized in the FNN and compared with other models commonly used for classification tasks capture results of greater accuracy than the current state of the art for this type of problem (84.32% for immunotherapy, and 88.64% for cryotherapy), and extract fuzzy rules from the problem database. It was found that the hybrid approach based on neural networks and fuzzy systems can be an excellent tool to aid the prediction of cryotherapy and immunotherapy treatments.

A fuzzy neural network for pattern classification and feature selection

2002 ◽  
Vol 130 (1) ◽  
pp. 101-108 ◽  
Author(s):  
Rui-Ping Li ◽  
Masao Mukaidono ◽  
I.Burhan Turksen
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Pattern Classification ◽  
Fuzzy Neural Network ◽  
Fuzzy Neural

Support-vector-based fuzzy neural network for pattern classification

2006 ◽  
Vol 14 (1) ◽  
pp. 31-41 ◽  
Author(s):  
Chin-Teng Lin ◽  
Chang-Mao Yeh ◽  
Sheng-Fu Liang ◽  
Jen-Feng Chung ◽  
N. Kumar
Keyword(s):  
Neural Network ◽  
Pattern Classification ◽  
Fuzzy Neural Network ◽  
Support Vector ◽  
Fuzzy Neural

A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

2018 ◽  
Vol 106 (6) ◽  
pp. 603 ◽  
Author(s):  
Bendaoud Mebarek ◽  
Mourad Keddam
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Fuzzy Neural Network ◽  
Treatment Time ◽  
Calculation Model ◽  
Average Error ◽  
Network Approach ◽  
Neural Network Approach ◽  
Fuzzy Neural ◽  
Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

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