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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...
This paper proposes a PCA and ANN based approach to identify significant influential quality factors and modeling customer satisfaction for complex service processes. Firstly, the performance evaluation index system includes initial factors and customer satisfaction degree is proposed, and then the measurement data are collected by questionnaires. Secondly, by using PCA, several preceding principal...
The paper studies the application of principal component analysis and ANN (Artificial Neural Networks) for pre-warning of enterprise financial crisis, analyzes the factors of financial crisis, and constructs the model of the enterprise financial crisis with principal component analysis and ANN. It integrates simplifying of enterprise financial crisis index, dynamic learning of financial crisis knowledge...
Flood disaster management is an important part of flood risk assessment. A regional flood disaster risk assessment index system is established in this paper. Then principal component analysis (PCA) method and BP neural network are combined, and a regional flood disaster risk assessment of PCA-BP neural network model is established. PCA-BP neural network model analyze the loss of flood disaster about...
Irradiation has been used for food preservation in many areas, however the high dose of irradiation is able to influence the safety of food. Rice flour is a staple food hackneyed in our lives and a part of them are processed by irradiation. Here we offer a new method for fast discriminating the rice flour with different doses of irradiation based on visible-near infrared spectroscopy. We dealt with...
Visible and near infrared (NIR) spectroscopy was utilized to determine the growing areas of Tremella fuciformis. Principal component analysis (PCA) obtained the cluster plot which shows the difficulty to determine the growing area by the first three principal components. Least-square support vector machine (LS-SVM) was used to establish the calibration model. Successive projections algorithm (SPA)...
The article develops a BP network for trip chaining pattern recognition based on the data obtained from Beijing Resident Trip Survey. First a set of socioeconomic and demographic factors related to traveller information which potentially influence trip-chaining patterns are pre-treated through principle components analysis, therefore seven variables are selected as input variables of neural network,...
The paper gives a new value model based on main component analysis method and fuzzy theory to value marketing audit. Because the number of marketing audit value' indexes is too many, the calculation is very complex. This paper uses the main component analysis method to play down the complication. Because of the subjective effect of value people, the fuzzy theory is used to improve the objectivity...
As the Government's macroeconomic regulation and control means, finance plays a significant role, but finance bears some certain risks. How to manage and prevent this risk is directly related to the survival and development of the whole country. This paper focuses on the combined model application research, fiscal risk early-warning based on principal component analysis and BP neural network. The...
Water quality evaluation is an important basic work for the water environmental management and protection. This paper presents a new water quality monitoring method based on principal component analysis-wavelet neural network combined model. First, the establishment of water quality evaluation index system, and then using principal component analysis to remove the relevance, overlap information of...
About the risk evaluation of network security, a new assessment method based on Nonlinear Principal Component Analysis (NLPCA) is given. The principle and process of NLPCA-RBF is introduced in detail. At last, its superiority is indicated by example. It not only can reduce the dimension of input vector, but also can reserve the nonlinear characteristic of the network by nonlinearity. It is a new evaluation...
This article addresses psychological characteristics, performance of victims and the impact factors of individual psychological crisis responses after a major disaster, and set forth the significance of applying psychological crisis forewarning evaluation in the post-disaster reconstruction. The established psychological crisis forewarning index system is in conformity with the principles of sensitivity,...
According to complexity and comprehensibility of factors which affect road traffic safety, we use the method of principal component analysis to refine new factors which are linearly independent, then we forecast road traffic accident according to principal component by BP neural network simulation, analyse the relationship between traffic accident evaluating index and the causes of traffic accident,...
The evaluation of information system is a complex system. Domestic and foreign scholars generally agreed that the evaluation of information system is a difficult task. In this paper, we combined the principal component analysis (PCA) and neural network (NN) in order to evaluate the information system. Using principal component analysis to extract availability information and to solve a principal component,...
A significant number of control loops in process plants perform poorly due to control valve stiction. Stiction in control valves is the most common and long standing problem in industry, resulting in oscillations in process variables which subsequently lowers product quality and productivity. Developing a method to detect valve stiction in the early phase is imperative to avoid major disruptions to...
Customer segmentation is the basis of the customer relationship management (CRM). For the retail business, customer segmentation through certain methods will help the good implementation of customer relationship management. The customer segmentation based on the purchase behavior may bean effective method of choice. Principal component analysis (PCA) is a method of multivariate statistical analysis...
Risk assessment, a critical process for project evaluation and investment, is one of the most important steps in IT projects implementation. Current qualitative and quantitative methods include analytic hierarchy process (AHP), artificial neural network (ANN), causality analysis, fuzzy comprehensive evaluation, etc. But most of them suffer from one of these two shortcomings: 1) the evaluation process...
Because artificial neural network possesses powerful capabilities in filtering and learning adaptability and functions of multi-I/O, thus, this method is expected to be more efficient than those of classical statistics modeling constructs. In the domain of artificial neural network, probabilistic neural network demonstrates broader and much more generalized capabilities, as of now, the latter has...
This paper prepares a review of ICA based approaches that are used for separation of components in functional MRI sequences. In previous works, the FastICA and the Infomax algorithms are investigated in more details; therefore, in this paper we focus on methods such as "radical ICA", "SDD ICA", "Erica" and "Evd" for separation purposes. This comparative study...
To further improving the analysis accuracy of Artificial Neural Networks (ANN) model for quantitative analysis of seven-component alkane gaseous mixtures composed of methane, ethane, propane, isobutane, n-butane, isopentane, and n-pentane, the Kernel Principal Component Analysis (KPCA) technique was proposed to couple with it. The gaseous mixtures were measured by a novel Acousto-Optic Tunable Filter...
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