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This paper is based on data analysis and literatures, land use system, demographic factors, and economic development situation, fiscal and financial policies which have influence on the price of the house are studied. In order to discuss housing price purely on the basis of statistical data, the main factors and their weights are calculated based on the survey of house price and usage of grey theory...
An extended evaluation method for the decision-making problems of alternatives is introsuced by means of extension theory. The extended evaluation method can carry out the decision-making problems of the alternatives based on matter-element models and extended relational degrees. The matter-element model of the desired alternative (ideal alternative) and the matter-element models of alternatives are...
The goal of this research is to identify the significant factors affecting the firm performance and estimate the system behavior in different operating conditions. By determining the statistical relations of the productivity and effectiveness of the firm with these factors, a decision-making framework can be provided to improve the system performance within the competitive strategy of the whole supply...
Aiming at fuzziness of water-saving irrigation technology evaluation and the independence and incompatibility of each single index, a entropy-weight based fuzzy matter-element model for paddy field water-saving irrigation technology optimization was established based on the combination of fuzzy matter-element and the entropy theory in paddy field water-saving irrigation technology evaluation in the...
This paper presents an application of a neuro-fuzzy modeling approach in order to characterize essential behavior of biological processes. The gathered information from experiments was employed to develop a fuzzy model for an enzyme-catalyzed esterification process. The accuracy of developed model was validated by comparing the response of the model and actual data from experiments. A model-based...
C-regression models are known as very useful tools in many fields. Since now, many trials to construct c-regression models for data with uncertainty in independent and dependent variables have been done. However, there are few c-regression models for data with uncertainty in independent variables in comparison with dependent variables now. The reason is as follows. The models are constructed using...
In this paper, the method that measuring dataset of knitted yarns is clustered using improving fuzzy kernel c-Means (FKCM) clustering algorithm is proposed. In FKCM clustering algorithm, the data of low dimension input space is mapped to high dimension feature space, FCM clustering algorithm is performed in feature space, then the constraint optimization distance matrix and membership matrix of testing...
The method that based on semi-structural decision-making fuzzy analysis theory to establish fuzzy optimization model for complexity decision making is with such features: accurate results and widely applications, while with a complicated computing procedure, and low efficiency in manual computing. For those above, auxiliary computing system of semi-structural decision-making fuzzy analysis was developed...
We present a new approach to parameters identification of the fuzzy regression model with respect to the e-insensitive estimator in this paper. The proposed method firstly employs the improved fuzzy c-mean clustering algorithm to carry out fuzzy partition of input-output data pairs, which ascertains the membership functions of fuzzy system. Secondly, the quadratic convex optimization similar to the...
Value at risk (VaR) is a measure for senior management that summarises the financial risk a company faces into one single number. In this paper, we consider the use of fuzzy histograms for quantifying the value-at-risk of a portfolio. It is shown that the use of fuzzy histograms provides a good method of value-at-risk estimation for a portfolio of stocks. The conditional parameters of the model are...
To overcome the disadvantage of the imperfect and uncertain data and redundancy node, reduce energy efficiency of communications and data processing, an optimization model based on agent distributed computation is proposed. In this model, vague set theory is used to optimize and reduce data. Furthermore, it is applied to intelligent information processing of wireless sensor networks (WSN) as clusters...
Fuzzy c-regression models (FCRM) performs switching regression based on a Fuzzy c-means (FCM)-like iterative optimization procedure, in which regression errors are also used for clustering criteria. In data mining applications, we often deal with databases consisting of mixed measurement levels. The alternating least squares method is a technique for mixed measurement situations, in which nominal...
In this paper we explore the use of weights in the generation of fuzzy models. We automatically generate a fuzzy model, using a three-stage methodology: (i) generation of a crisp model from a decision tree, induced from the data, (ii) transformation of the crisp model into a fuzzy one, and (iii) optimization of the fuzzy modelpsilas parameters. Based on this methodology, the generated fuzzy model...
The assessment of a theory is the main objective of scientists. Theories are always introduced by models, and model selection is applied to many various fields of scientific studies in order to corroborate or verify the theory as the winning one among a set of competing hypotheses. Different criteria are taken as bases to select one model among several parallel models in both statistical and visual...
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