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This paper focuses on the effective analysis of the mine gas emission monitoring data, so as to realize the accurate and reliable mine gas emission prediction. Firstly, a weighted multiple computing models based on parametric t—norm is constructed. And a new mine gas emission combination forecasting method is proposed. The BP neural network model and the support vector machine were used...
According to the characteristics of car ownership prediction influenced by multi-factor and non-linear, a combination forecasting model was proposed based on principal component analysis (PCA) and BP neural network for the purpose of car ownership prediction. Take the national car ownership as an example, the principal component analysis is carried out on the factors affecting the car ownership, and...
with the development of Internet, many companies announced the stock information by various medium, and stock buyers comment that information as well as make rational investment strategies to maximize their profit. At present, many retail investors, who lack of channels to obtain real information, scarce professional knowledge of investment theory and easy to be affected by public opinion of Internet,...
This paper proposes a med-long term runoff forecasting model based on the principal component analysis (PCA) and the improved BP Neural Network. PCA was utilized to eliminate the relevance between input data, reducing input dimension and effectively reducing the model's structural complexity, improving the model's learning efficiency and forecast performance. The proposed model was predicted and verified...
High concentration photovoltaic is a new type of solar power generation mode, which has better photoelectric conversion rate but is more vulnerable to weather factors. Therefore, accurate and efficient forecasting methods have important significance of increasing the security and stability of the solar power station. This paper focuses on the short-term forecasting method which aims at forecasting...
The accurate power output forecasting is advantageous to improving the reliability of power system. This paper presents a new power forecasting model based on grey neural network and Markov chain. In grey neural network, it gains the power at the corresponding time as the forecasting result. As getting the relative prediction residual errors of the forecasting sample data with grey neural network,...
In the paper, a scheme is proposed to predict whether the new employees could match their jobs or not, and discrete interval type-2 fuzzy set on time-varying universe is used for evaluating the experts' view of the employees' performance. The evaluation results are sent into a BP neural network to get the predicted value. Finally, the Markov model is adopted to revise the result and achieve a final...
Accurate and real-time tidal level forecasting information is significant for ensuring safety of navigation and port operation. The conventional method for tidal level forecasting is the harmonic analysis method which only considers the effect of celestial bodies to tidal level. However, the cause of tidal level change is intricate which can be also influenced by environmental factors such as wind,...
BP Neural Network can forecast short-term electricity price, while it is necessary to explore technique to tune the back propagation learning algorithm either for better generalization, or for faster training. The paper proposed enhanced BP Neural Network to forecast electricity price, in which we replaced back propagation algorithm of BP Network with genetic simulated annealing algorithm (GSAA)....
The degree of accuracy in predicting the photovoltaic power generation plays an important role in appropriate allocations and economic operations of the power plants based on the generating capacity data gathered from the geographically separated photovoltaic plants through network. In this paper, a forecasting model is designed with an optimization algorithm which is developed with the combination...
Accurately forecasting future call volumes is critical for scheduling of a call center. This thesis develops a PCA-BP model to forecast future call volumes of half-hour periods. the approach adopted firstly uses principle component analysis to eliminate the intraday correlations between the call volumes of 48 consecutive half-hour periods and to simplify the structure of BP neural network by dimension...
Basing on statistic data of general aviation flying hours of the year 1990–2009, using BP neural network to forecast general aviation flying hours. Analysis BP neural network's principle and normalize the data, establish neural network forecast model of general aviation flying hours time-series data, design the network parameters, and learn an train the history data of the year 1990–2007, test data...
To resolve the problem of short-term power load forecasting, we propose a self-adapting particle swarm optimization (PSO) algorithm to optimize the error back propagation (BP) neural network model. The proposed model is called PSO-BP model which employs PSO to adjust control parameters of BP neural network. In order to verify the performance of PSO-BP, the practical datum of a city in China are selected...
In order to improve the accuracy of power load forecasting, this paper proposes a hybrid model based on Ensemble Empirical Mode Decomposition (EEMD), least square-support vector machine (SVM) and BP nature network as a short-term load forecasting model. At first, the actual power load series is decomposed into different new series based on EEMD. Then the right parameters and kernel functions are chosen...
The electricity is closely related to the residents' living. The satisfaction of living and industrial electricity consumption is directly related to economic development and social stability. Accurately predicting urban electricity consumption for the foreseeable future there will help decision makers make the adjustment and specific work. In recent years, in order to solve the problem of the forecasting...
This paper studies the application of Huang transform to time series forecasting. Firstly, the time series are decomposed into a finite and often small number of intrinsic mode functions (IMF) and one residual function (RF). IMF components can characterize local properties and RF components can represent the total trend of the origin time series. Secondly, BP neural network is applied to forecast...
Traffic analysis and prediction is one of the core contents in the feasibility study of highway construction project [1]. It has the vital significance to the highway construction and road networks development. The traditional traffic volume prediction, as four steps prediction method [2] represented, have many uncertain factors to make the deviation between final forecast results and actual situation...
In this paper, from the Angle to predict, take hydro-generating operation condition parameters (head, power) as input sample, take unit head cover vibration as output sample, create BP and GANN neural network prediction model. Train the established models, through comparing the two models. GANN model Has better precision.
In order to obtain the law of the building settlement and forecast it effectively, neural network model was established for building settlement forecasting based on measured data, and an engineering example is shown to test and verify. Firstly, data of building settlement measured were normalized; embedding dimension was selected to establish the leaning samples. Mean square error (MSE) and mean absolute...
Determining of the torpedo's service year reasonably, it is an effective way to reduce the military expenses expenditure, and forecast the torpedo economic life. We can forecast the data of exponential use maintenance cost by using the grey metabolism GM(1,1) model. In order to improve the prediction precision, the data was divided into several groups, and prediction residual was modified by using...
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