An expert decision support system for production control
Decision Support Systems
The nature of statistical learning theory
The nature of statistical learning theory
Measures of uncertainty in expert systems
Artificial Intelligence
An extended rule-based inference for general decision-making problems
Information Sciences: an International Journal
The use of ARIMA models for reliability forecasting and analysis
Proceedings of the 23rd international conference on on Computers and industrial engineering
Combining belief functions when evidence conflicts
Decision Support Systems
Belief functions and default reasoning
Artificial Intelligence
Modeling vague beliefs using fuzzy-valued belief structures
Fuzzy Sets and Systems - Special issue on fuzzy numbers and uncertainty
Fuzzy ARIMA model for forecasting the foreign exchange market
Fuzzy Sets and Systems
Application of Dempster—Shafer theory in condition monitoring applications: a case study
Pattern Recognition Letters
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Journal of Intelligent and Robotic Systems
Using Dempster-Shafer's Theory of Evidence to Combine Aspects of Information Use
Journal of Intelligent Information Systems
Fault Prediction Modeling for Software Quality Estimation: Comparing Commonly Used Techniques
Empirical Software Engineering
Pattern Recognition Letters
Software reliability forecasting by support vector machines with simulated annealing algorithms
Journal of Systems and Software
Evidential reasoning approach for bridge condition assessment
Expert Systems with Applications: An International Journal
Analyzing the combination of conflicting belief functions
Information Fusion
Expert Systems with Applications: An International Journal
Reasoning with imprecise belief structures
International Journal of Approximate Reasoning
Analysis of evidence-theoretic decision rules for pattern classification
Pattern Recognition
Pattern Recognition Letters
A neural network classifier based on Dempster-Shafer theory
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Belief rule-base inference methodology using the evidential reasoning Approach-RIMER
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Multiple-attribute decision making under uncertainty: the evidential reasoning approach revisited
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Optimization Models for Training Belief-Rule-Based Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
System reliability forecasting by support vector machines with genetic algorithms
Mathematical and Computer Modelling: An International Journal
Recurrent neural networks and robust time series prediction
IEEE Transactions on Neural Networks
A belief-rule-based inventory control method under nonstationary and uncertain demand
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
In this paper, a new fault prediction model is presented to deal with the fault prediction problems in the presence of both quantitative and qualitative data based on the dynamic evidential reasoning (DER) approach. In engineering practice, system performance is constantly changed with time. As such, there is a need to develop a supporting mechanism that can be used to conduct dynamic fusion with time, and establish a prediction model to trace and predict system performance. In this paper, a DER approach is first developed to realize dynamic fusion. The new approach takes account of time effect by introducing belief decaying factor, which reflects the nature that evidence credibility is decreasing over time. Theoretically, it is show that the new DER aggregation schemes also satisfy the synthesis theorems. Then a fault prediction model based on the DER approach is established and several optimization models are developed for locally training the DER prediction model. The main feature of these optimization models is that only partial input and output information is required, which can be either incomplete or vague, either numerical or judgmental, or mixed. The models can be used to fine tune the DER prediction model whose initial parameters are decided by expert's knowledge or common sense. Finally, two numerical examples are provided to illustrate the detailed implementation procedures of the proposed approach and demonstrate its potential applications in fault prediction.