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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...
DNA methylation (DNAm) is an epigenetic mechanism used by cells to control gene expression, and identification of DNAm biomarkers can assist in early diagnosis of cancer. Identification of these biomarkers can be done using CpG (Cytosine-phosphate guanine) sites, or particular regions in DNA. Previous machine learning methods known as MS-SPCA and EVORA have been used to link DNAm biomarkers to specific...
Once a multivariate model is developed, it can be combined with tools and techniques from univariate statistical process control to form multivariate statistical process control tools. It allows development of advanced process monitoring strategies. In the current study, copper plant history data with multiple variables was successfully treated by principal component analysis to detect abnormal process...
Rainfall is an important factor in the agricultural process. Several methods to predict the rainfall have been carried out in Indonesia, such as the modeling of Statistical Downscaling (SDS). SDS models might involve ill-conditioned covariates (large dimension and high correlation/multi collinear). This problem could be solved by a variable selection technique such as L1 regularization/LASSO or a...
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...
A new method for discriminating the varieties of natural textile fiber based on visible/near infrared spectroscopy (Vis/NIRS) was developed. In order to achieve the rapid identification of the varieties of natural fiber, four kinds of fiber of cotton, flax, silk and cashmere were selected for analysis. Firstly, the spectra with wavelength 350–1800nm of each variety fiber were scanned by spectrometer,...
In the paper, a forecast combination via dimension reduction techniques is applied to forecasting electricity spot prices. We propose to use a Factor Averaging (FA) method, which is based on a principle component approach. This methodology allows to extract information from a large panel of forecasts and helps to solve two important issues: collinearity of forecasts and common bias of different predicting...
In the development of drugs compounds suitable for human being, many experiments have to be conducted to ensure drugs safe consumption and generally takes almost 10 to 12 years for a particular drugs to enter the market from laboratory. Therefore, the pattern recognition in QSAR is significant for analyzing the data and developing several necessary models, so that only novel drugs candidate will be...
Study on the prediction of stock price has great theoretical significance and application value. Traditional stock forecasting methods cannot fit and analysis highly nonlinear, multi-factors of stock market well, there are problems such as the prediction accuracy is not high, the slow training speed etc. In order to improve the accuracy of stock price forecasting, this paper proposes a prediction...
Cloud providers and data centers rely heavily on forecasts to accurately predict future workload. This information helps them in appropriate virtualization and cost-effective provisioning of the infrastructure. The accuracy of a forecast greatly depends upon the merit of performance data fed to the underlying algorithms. One of the fundamental problems faced by analysts in preparing data for use in...
Aiming at the problem of lower forecast accuracy of traditional RBF neural network model, we suggest a new modeling method. First, pretreatment data are sampled using SPSS software. Principal component analysis (PCA) was applied to original data correlation analysis to remove the correlations between attributes and find the main influencing indicator to reduce the number of input layer nodes. The...
For the fault diagnosis problems of the underwater vehicle sensor systems, the solution is combined by the Principal Component Analysis (PCA) and Self-Organizing Fuzzy Cerebellar Model Articulation Controller (SOFCMAC). The signal prediction model approach based on PCA and SOFCMAC is proposed in this paper. According to the history data, it can predict the signal data in the future time using the...
Understanding performance bottlenecks of applications in high performance computing can lead to dramatic improvements of applications performances. For example, a key problem in GPU programming is finding performance bottlenecks and solving them to reach the best possible performance. These bottlenecks in GPU architectures span a variety of factors such as memory access latency, branch divergence,...
Multiple linear regression (MLR), the principal components analysis (PCA) and partial least squares (PLS) method are the traditional chemo metric methods in the near infrared spectral analysis. However", "these linear methods could not obtain the very good predicted accuracy. in this paper, the research on application of Fuzzy Pattern Recognition to qualitative analysis of Near infrared...
This paper uses the BP neural network forecast model based on principal component analysis to predict China's railways freight. It firstly regroups indexes affecting railways freight by principal component analysis as to make the dimensions of index reduced and unrelated, and then it makes use of BP neural network to built model, and predicts the railways freight. The forecast result indicates that...
This paper investigates the interindividual variability of underlying glucose dynamics using multivariate statistical analysis methods for subjects with type 1 diabetes mellitus. Here two types of glucose dynamics are defined, the general dynamics and the output-relevant predictive dynamics. The concerned important issues are whether the underlying glucose dynamics change from subject to subject?...
Using the method of artificial neural networks and principal component analysis (PCA) to study on a variety of numerical forecast products for the same precipitation forecast. The results showed that the fitting accuracy of the principal component analysis artificial neural network ensemble model is better than each sub-product and the experimental results of the independent samples also shows its...
The advent of cloud computing makes scientists to extend their research environments over supercomputers to on-demand and dynamically scalable resources. Science cloud becomes a trend in various scientific domains these days. However, it is difficult to provide optimal job execution environment rapidly and dynamically depending on user's demands. Therefore, it is very important to predict user's requirements...
Blast furnace ironmaking process (BFIP) can be considered as a grey system due to the complexity. In this paper, a new approach is proposed to predict the silicon content in blast furnace (BF) hot metal based on the multivariate grey model in grey theory. Principal component analysis (PCA) method is also used to deal with the high correlation relationship between different variables of BFIP. With...
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