Quotient space theory of problem solving, a formal model of granular computing, is generalized in the sense that topological structure is replaced by Cech's closure space. Some basic issues of granular computing, ...
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Quotient space theory of problem solving, a formal model of granular computing, is generalized in the sense that topological structure is replaced by Cech's closure space. Some basic issues of granular computing, such as the representation of real world at different levels of granularity, property preserving and the construction of granular world, are discussed in detail. It turns out that most of conclusions of the classical quotient space theory keep being valid, so intension and applicable fields are enriched and enlarged respectively.
Determination of the native state of a protein from its amino acid sequence is the goal of protein folding simulations, with potential applications in gene therapy and drug design. To predict a global minimum (GM) str...
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Determination of the native state of a protein from its amino acid sequence is the goal of protein folding simulations, with potential applications in gene therapy and drug design. To predict a global minimum (GM) structure of a given sequence is a difficult task. A genetic algorithm (GA) is an efficient approach to find lowest-energy conformation for HP lattice model. We have introduced some new operators (symmetric and cornerchange operators) to speed up the searching process and give the result more biology significance. The result shows these new operators improved the success of prediction, compared with standard GA for benchmark HP sequence up to 50 residues
World Wide Web has developed to an inherently distributed information system,which brings people great trouble in finding needed information although huge amount of information available on the *** search engine is a ...
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World Wide Web has developed to an inherently distributed information system,which brings people great trouble in finding needed information although huge amount of information available on the *** search engine is a very important tool for people to obtain information on the webs,but the low-precision and low-recall exist widely in current search *** effective and accurate intelligent search engine based on the expert systems technology has become the most important research *** paper analyzes the WWW with different granularities according to quotient space theory,gives summarization of search engine and describes an expert system built by Visual Prolog.
The theoiy of the quotient space is a new mathematical tool for the study of the different granularity *** uses a triple(X,f,T)to describe a problem,among which X stands for the domain of the problem,f stands for the ...
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The theoiy of the quotient space is a new mathematical tool for the study of the different granularity *** uses a triple(X,f,T)to describe a problem,among which X stands for the domain of the problem,f stands for the attribute of the domain,and T stands for the structure of the *** analysis and solution of the problem(X,f,T),along with the further analysis and study of the domain and its structure and attribute,help to the description of the different granularity world based upon the complete *** paper firstly introduces the theory of quotient space,and then focuses on the application of this theoiy through the granularity analysis of the searching in the WWW,which has successfully come to the definite result of different *** about the search engine also are presented.
The quotient space theory obtains a fusion model about semi-order structure under the condition of consistency information, harnessing topology relation among elements of the universe space and hierarchical structure....
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The quotient space theory obtains a fusion model about semi-order structure under the condition of consistency information, harnessing topology relation among elements of the universe space and hierarchical structure. It has great significance to forming a unified theory structure about information fusion technology. This paper gets a new semi-order structure fusion model under the condition of inconsistency information and proves incompleteness of its semi-order lattice . An example is presented in the paper at last as well as a new method is indicated for Bayesian network structure learning.
Let G be a graph and f: G→ G be a continuous map with at least one periodic point. Using the quote space method, the paper addresses that f is an equicontinuous map if and only if one of the following End(G)+2k+1 con...
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Let G be a graph and f: G→ G be a continuous map with at least one periodic point. Using the quote space method, the paper addresses that f is an equicontinuous map if and only if one of the following End(G)+2k+1 conditions holds: 1) {f jm(End(G)+2k)!}∞j=1 is uniformly convergent, in which m=1,2,…, End(G)+2k; and 2) There is a positive integer n esuring that {f jn}∞j=1 is uniformly convergent.
This paper concerns a greedy EM algorithm for t-mixture modeling, which is more robust than Gaussian mixture modeling when a typical points exist or the set of data has heavy tail. Local Kullback divergence is used to...
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This paper concerns a greedy EM algorithm for t-mixture modeling, which is more robust than Gaussian mixture modeling when a typical points exist or the set of data has heavy tail. Local Kullback divergence is used to determine how to insert new component. The greedy algorithm obviates the complicated initialization. The results are comparable to that of split-and-merge EM algorithm while the proposed algorithm is faster. Also the by product of a sequence of mixture models is useful for model selection. Experiments of synthetic data clustering and unsupervised color image segmentation are given.
Multivariate t-mixture modelling is more robust than Gaussian mixture modelling to a set of data containing a group or groups of observations with longer than Gaussian tails or a typical observations. To alleviate the...
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ISBN:
(纸本)0780384032
Multivariate t-mixture modelling is more robust than Gaussian mixture modelling to a set of data containing a group or groups of observations with longer than Gaussian tails or a typical observations. To alleviate the problem of local convergence of the traditional EM algorithm, a split-and-merge operation is introduced into the EM algorithm for multivariate t-mixtures. The split-and-merge equations are first presented theoretically and then a new merge method is acquired. Accordingly, a modified EM algorithm is constructed. Experiments of data clustering and unsupervised color image segmentation are given.
The patterns of EEG changes with the mental tasks performed by the subject. In the field of EEG signal analysis and application, the study to get the patterns of mental EEG and then to use them to classify mental task...
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ISBN:
(纸本)953184061X
The patterns of EEG changes with the mental tasks performed by the subject. In the field of EEG signal analysis and application, the study to get the patterns of mental EEG and then to use them to classify mental tasks has the significant scientific meaning and great application value. But for the reasons of different artifacts contained in EEG, the pattern detection in EEG produced from normal mental states is a very difficult problem. In this paper, independent component analysis is applied to EEG signals collected from different mental tasks .The experiment results show that when one subject performs a single mental task in different trails, the independent components of EEG are very similar. It means that the independent components can be used as the mental EEG patterns to classify the different mental tasks.
It usually needs complicated nonlinear operations to get the characteristics from the raw information inputted, and it is very difficult to find this kind of algorithm directly. The geometrical meaning of the multilay...
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ISBN:
(纸本)0780375084
It usually needs complicated nonlinear operations to get the characteristics from the raw information inputted, and it is very difficult to find this kind of algorithm directly. The geometrical meaning of the multilayer perceptron's neuron model indicates that classifying samples according to the requirements by constructing neural networks is equal to finding a collection of domains with which vectors of the preset sample sets are partitioned. But in some applications, such as time series forecasting including stock share forecasting, due to their preset sample sets may contain some exceptions and erroneous results, it is desired to introduce some self-adjusting and probabilistic decision-making mechanism to enhance the accuracy of classification. At the same time the mechanism can reduce the size of neural networks and speed up the recognition process. We discuss a self-adjusting and probabilistic decision-making mechanism for the covering algorithm. Based on the method, we developed a self-adjusting and probabilistic decision-making classifier and applied the software package to forecast the share index of Shanghai's stock market.
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