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Simulating Random Fuzzy Variable

Simulating Random Fuzzy Variable

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The Eighth International Conference on Information and Management Sciences

作者： Danni Guo~1 and Renkuan Guo~2 1 Kirstenbosch Research Center South African National Biodiversity Institute Private Bag X7 claremont 7735 Cape Town South Africa 2 Department of Statistical Sciences University of Cape Town Private Bag Rondebosch 7701 Cape Town South Africa

＜正＞In this paper,we discuss a statistical simulation approach to obtain a sequence of realizations of a random fuzzy ***,we extend the simulation scheme to random fuzzy Brownian process and the spatial random fuzzy... 详细信息

关键词： Credibility measure Probability measure Fuzzy variable Random fuzzy variable Statistical simulation

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Uncertain Canonical Process Driven Models and Data Analysis

Uncertain Canonical Process Driven Models and Data Analysis

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第九届中国不确定系统年会、第五届中国智能计算大会、第十三届中国青年信息与管理学者大会

作者： Renkuan Guo~1,Yanghong Cui~1,and Danni Guo~2 1 Department of Statistical Sciences,University of Cape Town,Private Bag,Rondebosch,7701,Cape Town, South Africa 2 Kirstenbosch Research Center,South African National Biodiversity Institute,Private Bag X7,claremont 7735, Cape Town,South Africa

Classical statistical modeling and data analysis are typically assuming certain distributional family governing the behavior of data *** driven force is either a hypothesized probability distributional family or a stochastic *** example,Gaussian process regression models have Gaussian process as their driven *** the new uncertainty statistical modeling and data analysis on the uncertain measure foundation should be developed similarly and *** this paper, based on the reviewing the basic elements to build up a Gaussian process regression model,we propose a framework of the uncertain canonical process driven models and data analysis.

关键词： Statistical model and analysis Gaussian process driven models uncertain measure uncertain calculus uncertain canonical process driven models

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Applications of Uncertain Canonical Process in Spatial Predictions

Applications of Uncertain Canonical Process in Spatial Predi...

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第九届中国不确定系统年会、第五届中国智能计算大会、第十三届中国青年信息与管理学者大会

作者： Danni Guo~1,Renkuan Guo~2,and Yanghong Cui~2 1 Kirstenbosch Research Center,South African National Biodiversity Institute,Private Bag X7,claremont 7735, Cape Town,South Africa 2 Department of Statistical Sciences,University of Cape Town,Private Bag,Rondebosch,7701,Cape Town, South Africa

The Quiver trees spread in arid Namibia and South *** order to investigate the spatial distribution pattern associated with climate changes, from 2002 to 2008,Quiver trees observational data were *** to the financial constraint,data set is not large enough and even uncompleted:only forty-three out of the originally designated fifty-three observational sites,which causes extreme difficulties in the spatial pattern *** our preliminary analysis,we found that the growth of the Quiver trees is robust to climate variables,say,rainfall and temperature and further that even for close sites geographically,the growths can be very diversified, which implies conventional kriging prediction or the inverse distance approach not working in the *** this paper,we define signal to noise ratio as a habitat measure for the Quiver Trees and engage the uncertain interpolation and extrapolation approaches including the uncertain canonical process regression and uncertain kriging prediction,for the habitat measure of the ten"missing"sites.

关键词： Quiver trees habitat measure habitat distance uncertain measure uncertain canonical process regression uncertain kriging prediction

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Uncertainty Decision Analysis

Uncertainty Decision Analysis

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第八届中国不确定系统年会

作者： Renkuan Guo~1,Yanghong Cui~1,and Danni Guo~2 1 Department of Statistical Sciences,University of Cape Town,Private Bag, Rondebosch,7701,Cape Town,South Africa 2 Kirstenbosch Research Center,South African National Biodiversity Institute, Private Bag X7,claremont 7735,Cape Town,South Africa

＜正＞Statistical decision theory is a framework with a probabilistic foundation,which admits the random uncertainty about the real world and human *** this paper,we propose a theoretical framework for the observational data oriented decision analysis under general uncertainty environments. We start with the three basic elements of an uncertainty decision problem,evolve naturally to the investigation on the unique characteristic and the fundamental roles of uncertainty distribution underlying the observational data in decision process,and finally end at the establishment of an uncertainty Bayesian data modeling under the Maximum Uncertainty Principle.

关键词： Statistical decision state space action space loss function risk uncertain measure the essential form of uncertain distribution uncertainty decision uncertainty Bayesian analysis

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Theory of λ algorithm

Theory of λ algorithm

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第九届中国不确定系统年会、第五届中国智能计算大会、第十三届中国青年信息与管理学者大会

作者： Yanghong Cui~1,Renkuan Guo~1,and Danni Guo~2 1 Department of Statistical Sciences,University of Cape Town,Private Bag,Rondebosch,7701, Cape Town,South Africa 2 Kirstenbosch Research Center,South African National Biodiversity Institute,Private Bag X7, claremont 7735,Cape Town,South Africa

Genetic algorithm(GA)is most famous and useful algorithm belongs to the class of Evolutionary algorithm(EA).However,often an objective function possesses numerous local optima,which could trap GA from moving toward the desired global *** provides extremely long length of binary string,unsure position of two chromosomes crossover,turbid mutation methods,which fearfully limit the efficiency of *** this paper,we propose a new metaheuristic optimization algorithm named asλ*** new algorithm utilizes strings of digits from member set {0,1,2,3,and 4} to represent the fitness values of candidate solutions(represented as vectors in n-dimensional Euclidean space).Theλalgorithm draws useful information from both repeated and unrepeated digits of strings(candidate solutions),to simulate global advanced schema towards final *** new algorithm only asks 3 or 4 digits to represent an unknown variable, but still could access very precision ***λcomparison and expansion operations instead of inefficient mutation operation,which allowed the strings select more efficient schema from the *** using crossover,stochastic coding strategy,population selection,PSO methods...all of the existing optimization methods,the new algorithm still could achieve the highest searching efficiency, better optimized than most of existing algorithm.

关键词： λalgorithm metaheuristic optimization five elements advanced schema original the highest searching efficiency

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