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We propose an unconstrained global continuous optimization method based on tabu search and harmony search to support the design of fuzzy linear regression (FLR) models. Tabu and harmony search strategies are used for diversification and intensification of FLR, respectively. The proposed approach offers the flexibility to use any kind of an objective function based on client's requirements or requests...
In this study, we present a new idea dealing with the analysis of fuzzy random variables (FRVs) being treated as samples of data. The proposed concept can be used to model various real-life situations where uncertainty is not only present in the form of randomness but also comes in the form of imprecision described in terms of fuzzy sets. We propose a hybrid approach, which combines a convex hull...
Estimating the coefficients of objective functions in multi-objective model is sometimes difficult in real situations. Mathematical analysis of statistical data is used to determine the coefficients. In various cases, the statistical data may not contain only randomness, but also fuzziness, which should be treated properly. Thus, this paper employs fuzzy random regression model to approximate the...
By introducing a novel membership constraint function, a new algorithm called fuzzy c-means switching regression model with generalized improved fuzzy partitions (GIFP-FCRM) is proposed. This algorithm seems less sensitive to noise and outliers than the classical fuzzy C switching regression model (FCRM), and provides a generalized model with the fuzziness index m for the fuzzy C switching regression...
Type-2 fuzzy sets are shown to be able to handle inter and intra uncertainties of group of experts about a concept, however one of their main difficulties is elicitation of their membership functions. This paper discusses a method for type-2 fuzzy membership function elicitation of labels used in a survey on tourism online satisfaction. The method is based on the implementation of factor analysis...
Electricity demand forecasting is known as one of the most important challenges in managing supply and demand of electricity and has been studied from different views. Electrical load forecast might be performed over different time intervals of short, medium and long term. Various techniques have been proposed for short term, medium term or long term load forecasting. In this study we employ Adaptive...
Econometrics is based on economic data while the data represented by fuzzy sets can not be dealt with classical time series methods. In this paper the author proposes a new kind of variable named fuzzy variable of econometric model based on fuzzy membership function. The gap between fuzzy mathematics and econometrics is connected by the concept of fuzzy variable. An example of multivariate linear...
Credit scoring model development became a very important issue as the credit industry has many competitions. Therefore, most credit scoring models have been widely studied in the areas of statistics to improve the accuracy of credit scoring models during the past few years. This study used three strategies to construct the hybrid FSVM-based credit scoring models to evaluate the applicant's credit...
A new fuzzy identification approach using support vector regression (SVR) and particle swarm optimization algorithm (PSOA) is presented in this paper. Firstly positive definite reference function is utilized to construct a qualified Mercer kernel for SVR. Then an improved PSOA is developed for parameters selection of SVR, in which the number of support vectors and regression accuracy are regarded...
We discuss the historical perspective of concepts of fuzziness and randomness, with emphasis on fuzzy random variables. We will describe the analysis of the interpretation, modeling, and impact of fuzzy random variables. Examples will be given of scenarios modeled by fuzzy random variables. Also, main approaches using fuzzy random variables will be discussed. The theoretical concept of expected value...
This study presents an integrated fuzzy regression, computer simulation and time series framework to estimate and predict electricity demand for seasonal and monthly changes in electricity consumption especially in developing countries such as China and Iran with non-stationary data. Furthermore, it is difficult to model uncertain behavior of energy consumption with only conventional fuzzy regression...
The number of death and injury in the traffic accident is continue increasing year by year in China, which brings a great lost for social economy. In this paper we mainly studied on the data of traffic accident and follow volume in a certain section of road. And based on the theory of fuzzy mathematics and linear regulation to construct the fuzzy linear regression prediction model, and make an evaluation...
In recent years the number of death and injury in the traffic accident is continue increasing year by year in China, which brings a great lost for social economy. In this paper we mainly studied on the data of traffic accident and follow volume in a certain section of road. Fuzzy regression analysis using fuzzy linear models with symmetric triangular fuzzy number coefficients has been formulated earlier...
Some financial variables can always be observed with perturbations and be expected in the imprecise sense because of the fluctuation of financial markets. Therefore, this paper introduces fuzzy techniques, and gives a fuzzy currency options bounds pricing model. By denoting four input variables in the Garman-Kohlhagen model as triangular fuzzy numbers, the currency option price will turn into a fuzzy...
Based on data collected previously on the electricity market of the East China, we use stepwise regression method to find the approximate expression of the active power flow of each power sets on East Chinapsilas certain electrical network. Then classified discussion is carried out according to the difference of the capacity in and out of merit in order to obtain a simple and reasonable rule for calculating...
Qingdao is a representative coastal city, whose inshore water quality is in a deterioration tendency influenced by anthropogenic activities. Recent evolution of water quality was quantitatively analyzed by adopting fuzzy comprehensive evaluation method, and its predominant influence factors were determined by a multi-regression model. The results show that: (1)Qingdao inshore water quality worsened...
Regression is an important prediction method to establish models between variables. The primitive regression algorithms ignore the sample weights, and consider all samples play an equal role in regression. But this kind of algorithms often loses efficacy when dealing with outliers, since outliers disturb the regression models greatly. For traditional switching regression, sample membership varies...
Long-term load forecasting has a vital role in generation, transmission and distribution network planning. Traditional studies for long-term load forecasting were based on regression method, which could not provide a true representation of power system behavior in a volatile electricity market. The purpose of this paper is to introduce two approaches based regression method and artificial neural network...
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