Two views of belief: belief as generalized probability and belief as evidence
Artificial Intelligence
Robust reasoning: integrating rule-based and similarity-based reasoning
Artificial Intelligence
Measures of uncertainty in expert systems
Artificial Intelligence
Recursive EM and SAGE-inspired algorithms with application to DOA estimation
IEEE Transactions on Signal Processing - Part I
On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty
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
Optimization Models for Training Belief-Rule-Based Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A sequential learning algorithm for online constructing belief-rule-based systems
Expert Systems with Applications: An International Journal
ICIRA '09 Proceedings of the 2nd International Conference on Intelligent Robotics and Applications
New model for system behavior prediction based on belief rule based systems
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Inference analysis and adaptive training for belief rule based systems
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Condition-based maintenance of dynamic systems using online failure prognosis and belief rule base
Expert Systems with Applications: An International Journal
A belief-rule-based inventory control method under nonstationary and uncertain demand
Expert Systems with Applications: An International Journal
Structure learning for belief rule base expert system: A comparative study
Knowledge-Based Systems
On the inference and approximation properties of belief rule based systems
Information Sciences: an International Journal
A novel belief rule base representation, generation and its inference methodology
Knowledge-Based Systems
Construction of a new BRB based model for time series forecasting
Applied Soft Computing
Hi-index | 12.06 |
A belief rule base inference methodology using the evidential reasoning approach (RIMER) has been developed recently, where a new belief rule base (BRB) is proposed to extend traditional IF-THEN rules and can capture more complicated causal relationships using different types of information with uncertainties, but these models are trained off-line and it is very expensive to train and re-train them. As such, recursive algorithms have been developed to update the BRB systems online and their calculation speed is very high, which is very important, particularly for the systems that have a high level of real-time requirement. The optimization models and recursive algorithms have been used for pipeline leak detection. However, because the proposed algorithms are both locally optimal and there may exist some noise in the real engineering systems, the trained or updated BRB may violate some certain running patterns that the pipeline leak should follow. These patterns can be determined by human experts according to some basic physical principles and the historical information. Therefore, this paper describes under expert intervention, how the recursive algorithm update the BRB system so that the updated BRB cannot only be used for pipeline leak detection but also satisfy the given patterns. Pipeline operations under different conditions are modeled by a BRB using expert knowledge, which is then updated and fine tuned using the proposed recursive algorithm and pipeline operating data, and validated by testing data. All training and testing data are collected from a real pipeline. The study demonstrates that under expert intervention, the BRB expert system is flexible, can be automatically tuned to represent complicated expert systems, and may be applied widely in engineering. It is also demonstrated that compared with other methods such as fuzzy neural networks (FNNs), the RIMER has a special characteristic of allowing direct intervention of human experts in deciding the internal structure and the parameters of a BRB expert system.