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Granular computing is the key to granular neural networks, and in fact it is also the main problem in knowledge discovery and data mining. This paper addresses fuzzy information extraction and granular computing in granular neural networks in order that fuzzy rules can be discovered from fuzzy information which is difficult to be measured accurately with numerical data and furthermore the missing...
It accords with the human's intelligence for data processing that we do information granulation of time series and then study and analyze the time series on the different level of granularities. There are two kinds of traditional methods for the information granulation of temporal data. One implements the granulation by first dividing the time series into segments in terms of the granularity of the...
Grid computing deals with computationally intensive distributed resources on heterogeneous environment, so grid scheduling is a fundamental challenge and is critical to performance and cost. Traditional grid scheduling algorithms most use deterministic models. But grid environments in the real world are subject to many sources of uncertainty or randomness, such as network status, job execution costs,...
Supply chain, as an effective mode participating in the intense business competition, has been accepted by scores of enterprises, but there still exists great risks in supply chain, because of the characteristics of its special chain structure. This paper investigates the sources of supply chain risk, and the factors of supply chain risk assessment. Based on this, a fuzzy assessment model used to...
The data in traditional DEA model are limited to crisp inputs and outputs, which cannot be precisely obtained in many production processes or social activities. At the same time, the traditional DEA model can not evaluate by using some given reference units. This paper attempts to extend the traditional DEA model and establishes a fuzzy DEA model based on sample data evaluation. To establish this...
Eighty eight tobacco samples from six provinces in China, of which the contents of rare earth elements (REEs) were determined by microwave digestion-inductively coupled plasma mass spectrometry method. A fuzzy clustering method, fuzzy c-means (FCM), was used for classification of the different kinds of tobaccos based on their contents of REEs. The results show that FCM clustering analysis is a valid...
Ranking fuzzy numbers plays a very important role in decision making and some other fuzzy application systems. Many different methods have been proposed to deal with ranking fuzzy numbers. Constructing ranking indexes based on the centroids of fuzzy numbers is an important case. But some weaknesses are found in these indexes. The purpose of this paper is to give a new ranking index to rank various...
Setting up a reasonable ecological value evaluation system for residential quarters is important to improve people's living level. An evaluating index system with contains 2 factors, 12 sub-factors, and 50 elements is established. Then we use FAHP in determining the weights of the factors and sub-factors, which can reflect the human thinking style. At last, we establish a multi-grade fuzzy synthetical...
Colored fibers can be blended in a certain proportion to achieve a specific color. It is a very hard task for the colorist to find a good recipe to meet the final product without the aid of computer. In this paper, a color matching method for the colored fiber blends is discussed to substitute some manual work. The fuzzy C-mean cluster way is carried to group the color in the colored fiber blends...
In the human resources management system, to evaluate the capabilities of the enterprise leaders is a key and pressing step in the selection or the assessment of business management capabilities in the competitive world, while rough set method is suitable to meet this requirement. In order to provide a scientific and effective reference for discriminating the screening quality of the business leaders,...
The fuzzy c-means algorithm is a useful technique for clustering real s-dimensional data, but it can not be directly used for partially missing data sets. In this paper, the problem of missing data handling for fuzzy clustering is considered, and a statistical representation of missing attributes is proposed. The approach reduces the statistical analysis of missing attributes to the subsets of the...
This article set up a surface water quality fuzzy evaluation model, information entropy is used to determine weight of each factor of effect water quality, and then fuzzy comprehensive assessment is developed to evaluate Luanhe downstream surface water environment. The results showed that the method solved weight distribution problem better, and more reasonable and reliable conclusions are made by...
Fire risk is always very high on large public entertainment areas which contain a lot of people and facilities, and not only the big amount of the death and the wounded, but also the property lose would threaten the social safety. So it is important to analyze the fire risk assessment (FRA) on large public entertainment areas, the aim is to service the precaution methods and reduce fire risk. A kind...
The fuzzy time series is introduced by Song and Chissom to construct a pattern for time series with vague or linguistic value. Many methods using the interval and fuzzy logical relationship related with historical data have been suggested to enhance the forecasting accuracy. But they do not fully reflect the fluctuation of historical data. Therefore, we propose the interval rearranged method to reflect...
This paper first illustrates that a response function for the center of interval outputs may not be identical with that for the width of interval outputs in the interval regression model. Then we estimate the interval regression model using the best regression model for the center and the width of the predicted interval and minimizing the length of the symmetric difference between the observed and...
Intrusion of network which couldn't be analyzed, detected and prevented may make whole network system paralyze while the abnormally detection can prevent it by detecting the known and unknown character of data. A mixed fuzzy clustering algorithm that uses Quantum-behaved Particle Swarm Optimization (QPSO) algorithm and combines with Fuzzy C-means (FCM) is adopted in this paper and used in abnormally...
With respect to the subjective and nonlinear factors inherent in the importance identification of a fault tree analysis (FTA), a new importance measure of FTA is proposed based upon possibilistic information entropy. After investigating the possibilistic information semantics, measure-theoretic terms and entropy-like models, a two-dimensional framework is constructed by combining both the set theory...
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