Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
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
Newspaper demand prediction and replacement model based on fuzzy clustering and rules
Information Sciences: an International Journal
Locally recurrent neural networks for wind speed prediction using spatial correlation
Information Sciences: an International Journal
Time series forecasting with a non-linear model and the scatter search meta-heuristic
Information Sciences: an International Journal
Improving artificial neural networks' performance in seasonal time series forecasting
Information Sciences: an International Journal
Gaussian case-based reasoning for business failure prediction with empirical data in China
Information Sciences: an International Journal
A knowledge based real-time travel time prediction system for urban network
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
A sequential learning algorithm for online constructing belief-rule-based systems
Expert Systems with Applications: An International Journal
Deterministic vector long-term forecasting for fuzzy time series
Fuzzy Sets and Systems
Recursive EM and SAGE-inspired algorithms with application to DOA estimation
IEEE Transactions on Signal Processing - Part I
Similarity measures in fuzzy rule base simplification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Temperature prediction using fuzzy time series
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Prediction and identification using wavelet-based recurrent fuzzy neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Neuro-Fuzzy Inference System Through Integration of Fuzzy Logic and Extreme Learning Machines
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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
Optimization Models for Training Belief-Rule-Based Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A Novel Fuzzy System With Dynamic Rule Base
IEEE Transactions on Fuzzy Systems
A fuzzy neural network and its application to pattern recognition
IEEE Transactions on Fuzzy Systems
Online Updating Belief-Rule-Base Using the RIMER Approach
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
On the inference and approximation properties of belief rule based systems
Information Sciences: an International Journal
From model-based control to data-driven control: Survey, classification and perspective
Information Sciences: an International Journal
Core set analysis in inconsistent decision tables
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
Construction of a new BRB based model for time series forecasting
Applied Soft Computing
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To predict the behavior of a complex engineering system, a model can be built and trained using historical data. However, it may be difficult to obtain a complete and accurate set of data to train the model. Consequently, the model may be incapable of predicting the future behavior of the system with reasonable accuracy. On the other hand, expert knowledge of a qualitative nature and partial historical information about system behavior may be available which can be converted into a belief rule base (BRB). Based on the unique features of BRB, this paper is devoted to overcoming the above mentioned difficulty by developing a forecasting model composed of two BRBs and two recursive learning algorithms, which operate together in an integrated manner. An initially constructed forecasting model has some unknown parameters which may be manually tuned and then trained or updated using the learning algorithms once data become available. Based on expert intervention which can reflect system operation patterns, two algorithms are developed on the basis of the evidential reasoning (ER) algorithm and the recursive expectation maximization (EM) algorithm with the former used for handling judgmental outputs and the latter for processing numerical outputs, respectively. Using the proposed algorithms, the training of the forecasting model can be started as soon as there are some data available, without having to wait until a complete set of data are all collected, which is critical when the forecasting model needs to be updated in real-time within a given time limit. A numerical simulation study shows that under expert intervention, the forecasting model is flexible, can be automatically tuned to predict the behavior of a complicated system, and may be applied widely in engineering. It is demonstrated that if certain conditions are met, the proposed recursive algorithms can converge to a local optimum. A case study is also conducted to show the wide potential applications of the forecasting model.