On-line fault diagnosis of distribution substations using hybrid cause-effect network and fuzzy rule-based method

2000 ◽  
Vol 15 (2) ◽  
pp. 710-717 ◽  
Author(s):  
Wen-Hui Chen ◽  
Chih-Wen Liu ◽  
Men-Shen Tsai
Keyword(s):  
Fault Diagnosis ◽  
Fuzzy Rule ◽  
Rule Based ◽  
On Line

Sliding Mode Algorithm for On-line Learning in Fuzzy Rule-based Neural Networks

2008 ◽  
Vol 41 (2) ◽  
pp. 12793-12798 ◽  
Author(s):  
Andon V. Topalov ◽  
Okyay Kaynak ◽  
Nikola G. Shakev ◽  
Suk K. Hong
Keyword(s):  
Neural Networks ◽  
Sliding Mode ◽  
Fuzzy Rule ◽  
Rule Based ◽  
On Line ◽  
On Line Learning

A Neuron-Fuzzy Technique for Fault Diagnosis in Rotating Machinery

2011 ◽  
Vol 204-210 ◽  
pp. 2188-2191 ◽  
Author(s):  
Zheng Yao ◽  
Qing Xin Zhao
Keyword(s):  
Fault Diagnosis ◽  
Large Scale ◽  
Fuzzy Theory ◽  
Fuzzy Rule ◽  
Basic Structure ◽  
Rotating Machinery ◽  
Rule Set ◽  
On Line ◽  
Working Principle ◽  
Neural Network Theory

The on-line fault diagnostics technology for machines is fast emerging for the detection of incipient faults as to avoid the unexpected failure. On the basis of fault diagnosis theory and method, this paper presents a applications of techniques for fault detection and classification in rotating machinery based on fuzzy theory and neural network theory, the basic structure and working principle of the fault intelligent diagnosis system are introduced, the knowledge stored in the neuron-fuzzy system has been extracted by a fuzzy rule set with an acceptable degree of interpretability, the model of fuzzy fault diagnosis and the self-study principle are described. The practice proves that this is an effective method of large-scale and complicated electronic equipment, and it can also be applied to other fault diagnosis of complex systems and has certain portability.

scholarly journals Credit Card Fraud Detection Using Fuzzy Rule Based Classifier

2020 ◽  
pp. 41-44
Author(s):  
Prof. Sangeetha J. ◽  
Jegatheesh B. S. ◽  
Balaji B ◽  
Hemnath N
Keyword(s):  
Credit Card ◽  
Fuzzy Rule ◽  
Fraud Detection ◽  
Financial Fraud ◽  
Rule Based ◽  
The Everyday ◽  
On Line ◽  
Web App ◽  
Rule Based Classifier ◽  
Credit Card Usage

Fraud detection is an emerging topic of notable importance. Data mining strategies have been applied most considerably to the detection of insurance fraud, monetary fraud and financial fraud. This project will mainly focus on detecting fraudulent credit card transactions. Fraud detection in telecommunication systems, particularly the case of extraordinary imposed fraud, providing an anomaly detection technique supported by way of a signature schema, fraud deals with cases regarding criminal purposes that typically are different to identify, have additionally attracted a a tremendous deal of attention in latest years. The use of credit cards has dramatically increased because of a fast advancement inside the electronic commerce technology. Credit card will become the most popular mode of payment for each on line as properly as ordinary purchase, in instances of fraud related to it are also growing day through day. In this research sequence of operations in credit card transaction processing using a Fuzzy rule based classifier and accuracy is improved in the detection of frauds compared to other algorithms. A Naïve Bayes is initially trained with the everyday behaviour of a card holder. If an incoming credit card transaction is not accepted by the trained version with sufficiently excessive probability, it’s considered to be fraudulent. At the same time, it ensures that true transactions aren’t rejected. Supervised learning requires prior type to anomalies. In this research fuzzy rule primarily based category set of rules used for modelling real world credit card information statistics and detecting the anomaly usage of credit card information’s. Whenever anomaly credit card usage detected the system will capture the anomaly user face and freeze the anomaly user system. Django framework is used for web app creation.

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