灰色Verhulst型LS-SVM的构建及参数估计方法

为提高灰色Verhulst模型的预测能力,文章用统计学习理论的观点研究灰色Verhulst模型的建立问题。通过两种方式构造了以背景值序列和原始序列为训练样本的灰色Verhulst型LS-SVM,将一维样本空间里的Verhulst模型转化为一个二维特征空间里的LS-SVM模型,进而将Verhulst模型的灰参数的估计问题转化为一个LS-SVM模型的回归系数估计问题,实现了小样本体系下灰色Verhulst模型的建立和参数估计。实验结果表明该方法是可行且有效的,可有效提高Verhulst模型的推广性,比传统参数估计方法的预测精度更高。

DYNAMIC PREDICION OF FOREST FUEL LOADS BY GREY VERHULST MODEL

The variation of fuel loads after a fire for three forest types, phododendron -Larix gmetinii forest, herb--Larix gmelinii forest and herb--Betula plalyphlla forest , in the northern forest area of Daxing’anling region was discussed. The dynamic models were developed by gray theory for estimating the fuels loads of arbor- shrub, herbs’ grass, litter, and semi-decomposed litter, inflamma ble fuel and the total fuels in each forest type. After a fire, the inflammabIe fuel loads in phododendron-- Larix gmelinii forest and in the herb- - Betula platyphlla fores was estimated at 10.958 t/hm2and 10.473 t/hm2 respectively’ by 13 years later. and that was 12.297 t/hm 2 in herb--Larix gmeliniiforest by 7 years later.. It was predicated that a big fire may occur after 10 years based on inflammable fuel biomass accumulated.

Intelligent back analysis of geotechnical parameters for time-dependent rock mass surrounding mine openings using grey Verhulst model

In this paper,we present a new method of intelligent back analysis(IBA)using grey Verhulst model(GVM)to identify geotechnical parameters of rock mass surrounding tunnel,and validate it via a test for a main openings of−600 m level in Coal Mine“6.13”,Democratic People's Republic of Korea.The displacement components used for back analysis are the crown settlement and sidewalls convergence monitored at the end of the openings excavation,and the final closures predicted by GVM.The non-linear relation between displacements and back analysis parameters was obtained by artificial neural network(ANN)and Burger-creep viscoplastic(CVISC)model of FLAC3D.Then,the optimal parameters were determined for rock mass surrounding tunnel by genetic algorithm(GA)with both groups of measured displacements at the end of the final excavation and closures predicted by GVM.The maximum absolute error(MAE)and standard deviation(Std)between calculated displacements by numerical simulation with back analysis parameters and in situ ones were less than 6 and 2 mm,respectively.Therefore,it was found that the proposed method could be successfully applied to determining design parameters and stability for tunnels and underground cavities,as well as mine openings and stopes.

灰色GM(1,l)和Verhulst模型在吹填土地基沉降中的应用

在利用灰色GM(1,l)和Verhulst模型预测真空预压加固吹填土地基的最终沉降量时,建模序列直接影响预测结果的精度。结合某吹填土地基加固工程的沉降监测数据,探讨监测序列长度、建模序列长度对上述2种模型最终沉降量预测值及预测精度的影响,总结出不同模型预测地基最终沉降量时建模序列的选取规律。研究结果表明,上述2种模型预测地基最终沉降量的误差均小于1.0%,灰色GM(1,l)模型的适应性更好。

Non-equal-interval direct optimizing Verhulst model that x(n) be taken as initial value and its application

To overcome the deficiencies of the existing Verhulst GM(1,1) model, based on the existing grey theory, a non-equal-interval direct optimum Verhulst GM(1,1) model is built which chooses a modified n-th component x(n) of X(0) as the starting condition of the grey differential model. It optimizes a modified β value and the background value, and takes two times fitting optimization. The new model extends equal intervals to non-equal-intervals and is suitable for general data modelling and estimating parameters of the direct Verhulst GM(1,1). The new model does not need to pre-process the primitive data, nor accumulate generating operation (AGO) and inverse accumulated generating operation (IAGO). It is not only suitable for equal interval data modelling, but also for non-equal interval data modelling. As the new information is fully used and two times fitting optimization is taken, the fitting accuracy is the highest in all existing models. The example shows that the new model is simple and practical. The new model is worth expanding on and applying in data processing or on-line monitoring for tests, social sciences and other engineering sciences.

Industrial Engineering Analysis of Chinese Manufacturing Industry in Transition Period Based on Grey Predictions

In order to explore the characteristics and development strategies of Chinese manufacturing production system, the grey forecasting model GM( 1,1) and the grey verhulst dynamic model were built firstly. The prediction results show that Chinese manufacturing productivity would reach $ 32 806 per person in 2018,which indicates rapid development and lays the foundation for China to become the world's manufacturing power since the reform and opening up. However, it is predicted that Chinese manufacturing productivity would peak in 2018 based on the grey verhulst dynamic model,which reveals the resource configuration mode of Chinese manufacturing system could not prop up its increasing manufacturing capability. Furthermore the main reasons of this phenomenon were explored,which could be summarized as the lack of accumulation,integration of industrial engineering( IE)and information technology( IT), promoting mechanism of IE application as well as integration model of management innovation and technology innovation,etc. Finally,a series of strategies based on IE theory to solve these problems were given. This study provides an effective way to deal with the challenges and opportunities facing the Chinese manufacturing industry,meanwhile,it may contribute to the theoretical system of IE.

Network Security Situation Prediction Based on Improved Adaptive Grey Verhulst Model

Network security situation is a hot research topic in the field of network security. Whole situation awareness includes the current situation evaluation and the future situation prediction. However, the now-existing research focuses on the current situation evaluation, and seldom discusses the future prediction. Based on the historical research, an improved grey Verhulst model is put forward to predict the future situation. Aiming at the shortages in the prediction based on traditional Verhulst model, the adaptive grey parameters and equal- dimensions grey filling methods are proposed to improve the precision. The simulation results prove that the scheme is efficient and applicable.