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To predict the vibration displacement distributed on a vibrating surface, an r order Non-equidistant Grey Model (r-NGM) is proposed in this paper. This model is built by accumulating the initial discrete non-equidistant vibration displacement set with the r order Accumulated Generating Operation (r-AGO). The r-NGM is applied to a vibrating surface of a shaker to displacement prediction. The experimental...
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...
In high-dimensional modelling cases, a fuzzy modelling approach based on the grid-partitioning of fuzzy sets always meets great challenges, as it cannot avoid the problem of introducing a huge number of fuzzy rules. To tackle this issue, a new grid-partitioning based fuzzy modelling paradigm is proposed in this paper to construct a compact fuzzy system by including ‘short fuzzy rules’, in which only...
Friction Stir Welding (FSW) is a relatively new solid-state joining technique, which is versatile, environment friendly, and energy and time efficient. For a comprehensive understanding of the effects of process conditions, such as tool rotation speed and traverse speed, on characterisations of welded materials, it is essential to establish prediction models for different aspects of the materials'...
Cement rotary kiln calcining process is a kind of functional equipment for fuel combustion, heat exchange, and chemical reaction. A complex succession of chemical reactions takes place as the temperature rises. One can not establish a precise mathematical model of rotary kiln, so it is difficult to achieve its optimal control. In order to accurately reflect the system dynamic characteristics, we use...
To solve the scarcity of wireless spectrum, Cognitive Radio (CR) is proposed to let unlicensed wireless users (secondary users) dynamically find and access unused channels without interference to licensed users (primary users). The performance of the CR based Dynamic Spectrum Access (DSA) mechanism can be dramatically improved if the wireless spectrum is predictable, and many works has been done based...
This paper put forward a new method of the fuzzy rules and wavelet neural network model for short-term load forecasting. The neural call function is basis of nonlinear wavelets. We overcome the shortcoming of single train set of fuzzy rules. It can be seen from the example this method can improve effectively the forecast accuracy and speed. The forecast model was tested and the result showed that...
This paper put forward a new method of the variable structure artificial neural network model for mid-long term load forecasting. We overcome the shortcoming of single train set of ANN. It can be seen from the example this method can improve effectively the forecast accuracy and speed. The forecast model was tested and the result showed that it was an effective way to forecast mid-long term electric...
This paper put forward a new method of the fuzzy rules and wavelet neural network model for mid-long term load forecasting. The neural call function is basis of nonlinear wavelets. We overcome the shortcoming of single train set of fuzzy rules. It can be seen from the example this method can improve effectively the forecast accuracy and speed. The forecast model was tested and the result showed that...
This paper put forward a new method of the SVM and fuzzy rules model for short-term load forecasting. The neural call function is basis of nonlinear wavelets. We overcome the shortcoming of single train set of SVM. It can be seen from the example this method can improve effectively the forecast accuracy and speed. The forecast model was tested and the result showed that it was an effective way to...
This paper put forward a new method of the wavelet neural network model for mid-long term load forecasting. The neural call function is basis of nonlinear wavelets. We overcome the shortcoming of single train set of ANN. It can be seen from the example this method can improve effectively the forecast accuracy and speed. The forecast model was tested and the result showed that it was an effective way...
To solve the scarcity of wireless spectrum, cognitive radio (CR) is proposed to let unlicensed wireless users (secondary users) dynamically sense and access unused channels without generating interference to licensed users (primary users). The performance of CR-based dynamic spectrum access (DSA) mechanism can be dramatically improved if the wireless spectrum usage is predictable, and many works have...
This paper put forward a new method of the SVM and wavelet neural network model for short-term load forecasting. The neural call function is basis of nonlinear wavelets. We overcome the shortcoming of single train set of SVM. It can be seen from the example this method can improve effectively the forecast accuracy and speed. The forecast model was tested and the result showed that it was an effective...
In the study, back-propagation neural networks (BP-NN) theory and genetic algorithm (GA) were used to build a nonlinear prediction model reflecting the relationship between technics parameters of electric field aging and mechanical properties of LY12 aluminum alloy. In this model, electric field intensity, aging temperature and time were as input parameters. Tensile strength, yield strength and micro-yield...
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