A fuzzy neural classifier for pattern classification

This paper describes an enhancement of fuzzy lattice reasoning (FLR) classifier for pattern classification based on a positive valuation function. Fuzzy lattice reasoning (FLR) was described lately as a lattice data domain extension of fuzzy ARTMAP neural classifier based on a lattice inclusion measure function. In this work, we improve the performance of FLR classifier by defining a new nonlinear positive valuation function. As a consequence, the modified algorithm achieves better classification results. The effectiveness of the modified FLR is demonstrated by examples on several well-known pattern recognition benchmarks.

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Fuzzy Neural Classifier for Transformer Fault Diagnosis Based on EM Learning

Lecture Notes in Computer Science - Computational Intelligence ◽

10.1007/978-3-540-37275-2_29 ◽

2006 ◽

pp. 222-229 ◽

Cited By ~ 2

Author(s):

Hongsheng Su ◽

Qunzhan Li

Keyword(s):

Fault Diagnosis ◽

Fuzzy Neural ◽

Neural Classifier ◽

Transformer Fault

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

Big Data and Cognitive Computing ◽

10.3390/bdcc3020022 ◽

2019 ◽

Vol 3 (2) ◽

pp. 22 ◽

Cited By ~ 10

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.

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