-
[show abstract]
[hide abstract]
ABSTRACT: It is important to predict both observable and hidden behaviors in complex engineering systems. However, compared with observable behavior, it is often difficult to establish a forecasting model for hidden behavior. The existing methods for predicting the hidden behavior cannot effectively and simultaneously use the hybrid information with uncertainties that include qualitative knowledge and quantitative data. Although belief rule base (BRB) has been employed to predict the observable behavior using the hybrid information with uncertainties, it is still not applicable to predict the hidden behavior directly. As such, in this paper, a new BRB-based model is proposed to predict the hidden behavior. In the proposed BRB-based model, the initial values of parameters are usually given by experts, thus some of them may not be accurate, which can lead to inaccurate prediction results. In order to solve the problem, a parameter estimation algorithm for training the parameters of the forecasting model is further proposed on the basis of maximum likelihood algorithm. Using the hybrid information with uncertainties, the proposed model can combine together with the parameter estimation algorithm and improve the forecasting precision in an integrated and effective manner. A case study is conducted to demonstrate the capability and potential applications of the proposed forecasting model with the parameter estimation algorithm.
IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics: a publication of the IEEE Systems, Man, and Cybernetics Society 08/2012; · 3.01 Impact Factor
-
Expert Syst. Appl. 01/2012; 39:6140-6149.
-
[show abstract]
[hide abstract]
ABSTRACT: In order to determine the parameters of belief-rule-base (BRB) accurately, several optimization methods have been proposed for training BRB, on the basis of a generic rule-base inference methodology using the evidential reasoning (RIMER) approach. These optimization methods are implemented offline, and such are not suitable for training BRB in a dynamic fashion. In this paper, two recursive algorithms are proposed to update BRB online that can simulate dynamic systems. The main feature of the proposed algorithms is that only partial input and output information is required, which can be incomplete or vague, numerical or judgmental, or mixed. If the internal structure of a BRB is initially decided using expert judgments, domain-specific knowledge and/or commonsense rules, the proposed algorithms can be used to fine-tune the initial BRB online, once input and output datasets become available. Using the proposed algorithms, there is no need to collect a complete set of data before a BRB can be trained, which is necessary if the BRB is used to simulate a dynamic system. A numerical example and a case study are reported to demonstrate the potential of the algorithms for online fault diagnosis.
IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans 12/2011; · 2.12 Impact Factor
-
IEEE Transactions on Systems, Man, and Cybernetics, Part A. 01/2011; 41:1225-1243.
-
IEEE T. Fuzzy Systems. 01/2011; 19:636-651.
-
Expert Syst. Appl. 01/2011; 38:3937-3943.
-
IEEE Transactions on Systems, Man, and Cybernetics, Part A. 01/2011; 41:1268-1277.
-
Expert Syst. Appl. 01/2011; 38:5061-5080.
-
European Journal of Operational Research. 01/2011; 213:1-14.
-
Expert Syst. Appl. 01/2010; 37:1790-1799.
-
European Journal of Operational Research. 01/2010; 207:269-283.
-
Inf. Sci. 01/2010; 180:4834-4864.
-
Int. J. Systems Science. 01/2010; 41:783-796.
-
Expert Syst. Appl. 01/2009; 36:7700-7709.
-
[show abstract]
[hide abstract]
ABSTRACT: Gyro plays an important role in navigational systems and its drift has a direct influence on the precision. Therefore it is crucial that the gyro drift be forecasted precisely. In this paper, a hybrid modeling and forecasting approach based on the grey and the Box–Jenkins autoregressive moving average (ARMA) models is proposed to forecast the gyro drift. The results of experiments show that this method can forecast the drift precisely, which provides a basis for performance analysis and fault forecasting. Meanwhile, it can also be concluded that the hybrid method has a higher forecasting precision to the complex problems than the single method.
Chaos, Solitons & Fractals.
-
[show abstract]
[hide abstract]
ABSTRACT: In this paper, a novel reliability prediction technique based on the evidential reasoning (ER) algorithm is developed and applied to forecast reliability in turbocharger engine systems. The focus of this study is to examine the feasibility and validity of the ER algorithm in systems reliability prediction by comparing it with some existing approaches. To determine the parameters of the proposed model accurately, some nonlinear optimization models are investigated to search for the optimal parameters of forecasting model by minimizing the mean square error (MSE) criterion. Finally, a numerical example is provided to demonstrate the detailed implementation procedures. The experimental results show that the prediction performance of the ER-based prediction model outperforms several existing methods in terms of prediction accuracy or speed.
Expert Systems with Applications.
