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In this study, a deep denoising recurrent temporal restricted Boltzmann machine network is proposed for long-term prediction of time series. The network is a deep dynamic network model which is stacked by multiple denoising recurrent temporal restricted Boltzmann machines with strong modeling ability for complex high noise time series data. To better deal with high noise data, a random noise is added...
In this paper, the best irradiation technology for improving the quality of liquor by 60Co-γ irradiation was studied. Different doses of rays on liquor quality Law model was built by Bayesian regularization BP neural network method. This model was used for prediction and verification, and then the particle swarm optimization algorithm was used to predict the process parameters of the irradiation process...
Life prediction and failure analysis are necessary and critical for the aeronautical structures. In this paper, a general method is proposed to predict the fatigue life of smooth and circular-hole specimens using the equivalent initial flaw size (EIFS) concept. The proposed methodology is based on a previously developed method. Fatigue limit and threshold stress intensity factor are used to predict...
This paper proposes a prognostic-information-based joint order-replacement policy for a non-repairable critical system in service. The primary difference from existing work is to take the online condition monitoring data into consideration during the joint decision-making process. Towards this end, the system’s degradation trajectory is modeled by a Wiener process whose parameters are real-time estimated...
As the key of the prevalent prognostics and health management, remaining useful life prediction has attracted considerable attentions during the past decades. However, almost all of the existing remaining useful life prediction methods were implemented under the premise that the deteriorating systems were not maintained over the whole life cycle. For the deteriorating systems experiencing maintenance...
Corporation financial distress has been an important issue for study in the financial fields. This paper uses traditional BP neural network model and proposes PNN model to predicate financial distress. The sample consists of 276 companies listed on the Shanghai Stock Exchange and Shenzhen Stock Exchange over the period 2001–2010. Factor analysis is used to lower correlation and reduce dimensionality...
For most credit risk assessment models, decision attributes and history data are of great importance in terms of accuracy of prediction. Decision attributes can be classified into two types: numerical and categorical. As these two types have different characteristics, there will be interference if they are used simultaneously in the same model. By applying the case based reasoning (CBR) and artificial...
Many forecasting models have been developed for forecasting wind farm electricity output. In most situations, performance of models is problem-dependent. Thus, it is difficult for forecasters to choose the right technique for each unique situation. In order to overcome this problem, this paper integrates multiple models into an aggregated model to obtain further performance improvement. Firstly, three...
There are many multivariate forecasting models which incorporate weather indicators and other information for wind farm power output forecasting. In most situations, performance of these individual models is problem-dependent. Thus, it is difficult for forecasters to choose the right technique for unique situations. In this paper, firstly, indicators such as wind speed, and wind direction are analyzed...
In this paper, four new forecasting models ¨C univariate LS-SVM model and three hybrid models of ARIMA and LS-SVM models are introduced for wind power output forecasting. Historical data of 78 wind farms are used to compare and evaluate the performance of the best models. Empirical analysis indicates that the proposed univariate LS-SVM model and hybrid models can not significantly outperform linear...
Bi-dimensional empirical mode decomposition (BEMD) has been one of the core activities in image processing. Unfortunately, this promising technique is sensitive to boundary effect. Here, a new technique based on multivariate grey model termed as GM(1, 3) is developed for boundary extension in BEMD. More specifically, pixel values and coordinates of the image are regarded as characteristic data series...
Aiming at the problems of the wear condition monitoring, grey theory and auto-regressive combination forecasting model was put forward, and the combination model was build. The rough trend of the wear particle content change can be reflected through grey theory, and the detail of the change can be reflected through auto-regressive model. By testing and comparing a set of Ferro graphic data, the result...
In this paper, an adaptive model predictive control (MPC) weight coefficient methodology is proposed, and a stable output multi-variables zone(or range) control is implemented to guarantee the robustness of controlled process systems and the flexibility of manipulated variables. The weighted slack coefficients terms (WSCT) are added with nominal weight coefficients. The WSCT are adapted according...
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