Expert Systems with Applications: An International Journal
Online updating belief rule based system for pipeline leak detection under expert intervention
Expert Systems with Applications: An International Journal
Network forensics based on fuzzy logic and expert system
Computer Communications
A sequential learning algorithm for online constructing belief-rule-based systems
Expert Systems with Applications: An International Journal
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Inference based on notifications: a holonic metamodel applied to control issues
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special section: Best papers from the 2007 biometrics: Theory, applications, and systems (BTAS 07) conference
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Expert Systems with Applications: An International Journal
ICIRA '09 Proceedings of the 2nd International Conference on Intelligent Robotics and Applications
A new approach to the rule-base evidential reasoning: Stock trading expert system application
Expert Systems with Applications: An International Journal
New model for system behavior prediction based on belief rule based systems
Information Sciences: an International Journal
On the dynamic evidential reasoning algorithm for fault prediction
Expert Systems with Applications: 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
Optimization algorithm for learning consistent belief rule-base from examples
Journal of Global Optimization
Expert Systems with Applications: An International Journal
Belief rule-based methodology for mapping consumer preferences and setting product targets
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 stock trading expert system based on the rule-base evidential reasoning using Level 2 Quotes
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
Uncertain nonlinear system modeling and identification using belief rule-based systems
IUKM'13 Proceedings of the 2013 international conference on Integrated Uncertainty in Knowledge Modelling and Decision Making
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
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A belief rule-base inference methodology using the evidential reasoning approach (RIMER) has been developed recently, where a new belief rule representation scheme is proposed to extend traditional IF-THEN rules. The belief rule expression matrix in RIMER provides a compact framework for representing expert knowledge. However, it is difficult to accurately determine the parameters of a belief rule base (BRB) entirely subjectively, particularly, for a large-scale BRB with hundreds or even thousands of rules. In addition, a change in rule weight or attribute weight may lead to changes in the performance of a BRB. As such, there is a need to develop a supporting mechanism that can be used to train, in a locally optimal way, a BRB that is initially built using expert knowledge. In this paper, several new optimization models for locally training a BRB are developed. The new models are either single- or multiple-objective nonlinear optimization problems. The main feature of these new 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 a BRB whose internal structure is initially decided by experts' domain-specific knowledge or common sense judgments. As such, a wide range of knowledge representation schemes can be handled, thereby facilitating the construction of various types of BRB systems. Conclusions drawn from such a trained BRB with partially built-in expert knowledge can simulate real situations in a meaningful, consistent, and locally optimal way. A numerical study for a hierarchical rule base is examined to demonstrate how the new models can be implemented as well as their potential applications.