Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
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
The use of ARIMA models for reliability forecasting and analysis
Proceedings of the 23rd international conference on on Computers and industrial engineering
Time series forecasting with a non-linear model and the scatter search meta-heuristic
Information Sciences: an International Journal
Online updating belief rule based system for pipeline leak detection under expert intervention
Expert Systems with Applications: An International Journal
A sequential learning algorithm for online constructing belief-rule-based systems
Expert Systems with Applications: An International Journal
New model for system behavior prediction based on belief rule based systems
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
A new approach for time series prediction using ensembles of ANFIS models
Expert Systems with Applications: An International Journal
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
Online Updating Belief-Rule-Base Using the RIMER Approach
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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It is important to predict the future behavior of complex systems. Currently there are no effective methods to solve time series forecasting problem by using the quantitative and qualitative information. Therefore, based on belief rule base (BRB), this paper focuses on developing a new model that can deal with the problem. Although it is difficult to obtain accurately and completely quantitative information, some qualitative information can be collected and represented by a BRB. As such, a new BRB based forecasting model is proposed when the quantitative and qualitative information exist simultaneously. The performance of the proposed model depends on the structure and belief degrees of BRB simultaneously. Moreover, the structure is determined by the delay step. In order to obtain the appropriate delay step using the available information, a model selection criterion is defined according to Akaike's information criterion (AIC). Based on the proposed model selection criterion and the optimal algorithm for training the belief degrees, an algorithm for constructing the BRB based forecasting model is developed. Experimental results show that the constructed BRB based forecasting model can not only predict the time series accurately, but also has the appropriate structure.