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Combining the intelligent algorithm such as BP neural network and support vector maching (SVM) with traditional chemical method, this paper models the relationship between plant surface color and its pigment. Using the neural network model constructed above, people can figure out the content of plant pigments by getting the corresponding plant surface color information. Compared with the traditional...
Crustal deformation time series is a significant information source during the researches on continental deformation. In order to simulate the low frequency linear components which reflect dynamic trend as well as the high-frequency non-linear components which reflect disturbance, a prediction model based on Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) is presented. With optimization...
By analyzing the relation between mud logging data, well logging data and formation drillability, a novel method for predicting formation drillability based on particle swarm optimization and support vector machine (PSO-SVM) is proposed. The prediction model for formation drillability is established using the data of drilling pressure, rotary speed, hydraulic horsepower, bottom hole differential pressure,...
Stock market forecasting has attracted a lot of research interests in previous literature, and recent studies have shown that artificial neural networks (ANN) method achieved better performance than traditional statistical ones. ANN approaches have, however, suffered from difficulties with generalization, producing models that can overfit the data. This paper employs a relatively new machine learning...
Accurate forecasting of some economic indicators such as GDP is very useful. Aiming at the problem of modeling and forecasting of the nonlinear and complex economic system, an improved least square support machine model is proposed in this paper. A multi-scale chaotic search algorithm combined with GA is proposed for the optimum selection of model parameters. Time series data of the indicator to be...
A series of support vector machine (SVM) forecast experiments are carried out to reveal the relation between the SVM training sample size and SVM correct forecast ratio for simulation experiment results. Experiment results show that the SVM correct forecast ratio increases to some extent with the number of training samples becoming more and then keeps unchanged even if the SVM training sample number...
The irrigation water requirement forecasting is the basis for making scheduling program of water resource and allocating on water in irrigation area rationally and efficiently. The factors influencing the irrigation water are complex and nonlinear, and support vector machine (SVM) has many advantages on nonlinear small samples, therefore, this paper introduces SVM into forecasting irrigation water...
As a branch of data mining, data classification technology has got a widely use in science, engineering, finance and other areas. The key point of the classification techniques is to construct a classifier, in this paper, a non-liner classifier model based on RBF neural network is introduced to do the data classification, compared with traditional BP neural network, it is not only avoids complicated...
This paper proposes an algorithm which combines Particle Swarm Optimization (PSO) with Least Squares Support Vector Machines (LSSVM) to identify lithology by using well logging data. First of all, PSO is used for optimizing the main parameters of LSSVM, and then by using the optimized parameters to obtain a better PSO-LSSVM classification model which can be used to identify lithology with logging...
Accelerated Degradation Testing (ADT) is now adopted frequently to verify the reliability and life of high-reliable, long-life product. But ADT data analysis methods are still deficiency. Due to the excellent capable of little sample learning and nonlinear mapping, SVM prediction model is widely used in many fields. In this paper, a new degradation prediction method based on Support Vector Machines...
The theory of support vector regression (SVR) is introduced in this paper. And genetic algorithms (GAs) are adopted to optimize free parameters of support vector regression. Then we develop an optimal meteorological prediction model based on support vector Regression with genetic algorithms (SVRG). In this study, SVRG is applied to predict meteorology. The experimental results indicate that SVRG model...
Stock index prediction seems to be a challenging task of the financial time series prediction process especially in emerging markets with their complex and inefficient structures. Multivariate adaptive regression splines (MARS) is a nonlinear and non-parametric regression methodology and has been successfully used in classification tasks. However, there are few applications using MARS in stock index...
The traditional BS evaluation model for options assumes volatility as a constant, and is unable to explain phenomena such as leptokurtic distribution and volatility clusters. In order to supplement this shortcoming, scholars have begun to use linear and non-linear GARCH models to estimate volatility. However, a consistent result has not been achieved with empirical analysis of various different volatility...
The main objective of this study is to develop a predictor variable selection method based on rough set theory (RST) for runoff prediction, according to the different influence of different climate variables in different grid point on the runoff. The selected predictor variables were used as downscaling analysis predictors. Multiple linear regression (MLR), back propagation neural network (BPNN) and...
This paper studies the relation between chlorophyll-a and 10 environmental factors such as water temperature (T), COD, NH4+, NO3- TN, PO43+, TP, suspend solids (SS), Secci-depth (SD) and water depth (D) based on the monitoring data of 2005 in Taihu Lake. Three kinds of models are designed using the multiple regression statistical (MRS) method, the back propagation artifical neural network (BP ANN)...
A classification model is obtained after a classifier is trained on training data. Decision region is the region in which data are predicted the same class label by a classifier. Decision boundary is the boundary between regions of different classes. We view classification as dividing the data space into decision regions. The formal definitions of decision region and decision boundary are presented...
In order to experiment the performance of some popular ANN algorithms to OMIS (Operational Modular Imaging Spectrometer) hyperspectral image, three widely used ANNs, including Back Propagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), Fuzzy ARTMAP network and their improvements, are employed and compared. It is concluded that ANN classifiers perform much better than traditional...
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