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52 results for “TS fuzzy models”
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Research ArticleOpen accessIn the abstractHydrological Forecasting Using Hybrid Data-Driven ApproachYoungmin Seo, Sungwon KimThis study develops a hybrid model, EEMD-FANN, coupling feed Forward Artificial Neural Network (FANN) and Ensemble Empirical Mode Decomposition (EEMD) for improving the accuracy of daily river stage forecasting. An original river stage data is broken down into a residue and Intrinsic Mode Functions (IMFs) using the EEMD and different FANNs are developed as forecasting models for the decomposed IMFs … …American Journal of Applied Sciences, Vol. 13, Issue 8 · 2 Sep 2016 · 8,136 accessesView PDFCite
Research ArticleOpen accessIn the abstractESTIMATED AND ANALYSIS OF THE RELATIONSHIP BETWEEN THE ENDOGENOUS AND EXOGENOUS VARIABLES USING FUZZY SEMI-PARAMETRIC SAMPLE SELECTION MODELL. MuhamadSafiih, A. A. Kamil, M. T. Abu OsmanAn important progress within the last decade in the development of the selectivity model approach to overcome the inconsistent results if the distributional assumptions of the errors terms are made this problem is through the use of semi-parametric method. However, the uncertainties and ambiguities exist in the models, particularly the relationship between the endogenous and exogenous variables. A new framework … …American Journal of Applied Sciences, Vol. 11, Issue 9 · 15 Jul 2014 · 6,786 accessesView PDFCite
Research ArticleOpen accessIn the abstractCombined Pattern Mining for D3M Using FuzzyS. S. Dhenakaran, S. MaheswariMany algorithms and models were developed but the findings are not actionable and lack of soft power while solving the complex problems. Domain Driven Data mining is used a major efforts to promote the action ability of the knowledge discovery in the real world smart decision making. Combined mining is one of the common methods for analyzing complex data for … …American Journal of Applied Sciences, Vol. 10, Issue 12 · 1 Nov 2013 · 6,750 accessesView PDFCite
Research ArticleOpen accessIn the abstractA HYBRID INTELLIGENT MODEL FOR SOFTWARE COST ESTIMATIONWei Lin Du, Luiz Fernando Capretz, Ali Bou Nassif, Danny HoAccurate software development effort estimation is critical to the success of software projects. Although many techniques and algorithmic models have been developed and implemented by practitioners, accurate software development effort prediction is still a challenging endeavor in the field of software engineering, especially in handling uncertain and imprecise inputs and collinear characteristics. In this study, a hybrid intelligent model combining … …Journal of Computer Science, Vol. 9, Issue 11 · 28 Sep 2013 · 7,745 accessesView PDFCite
Research ArticleOpen accessIn the abstractPerformance of Multiple Linear Regression and Nonlinear Neural Networks and Fuzzy Logic Techniques in Modelling House PricesSiti Amri, Gurudeo Anand TularamHouse price prediction continues to be important for government agencies insurance companies and real estate industry. This study investigates the performance of house sales price models based on linear and non-linear approaches to study the effects of selected variables. Linear stepwise Multiple Regression (MR) and nonlinear models of Neural Network (NN) and Adaptive Neuro-Fuzzy (ANFIS) are developed and compared. The … …Journal of Mathematics and Statistics, Vol. 8, Issue 4 · 18 Dec 2012 · 8,430 accessesView PDFCite
Research ArticleOpen accessIn the abstractPath Loss Prediction Using Fuzzy Inference System and Ellipsoidal RulesMario Versaci, Salvatore Calcagno, Fabio La Foresta, Biagio CammarotoIt is well known as the prediction of radio wave path loss in urban environment plays a key role in order to correctly plan wireless systems and mobile communication networks. To obtain more flexible prediction models able to give accurate results, in recent years Soft computing Techniques has been exploited. In this study, a novel approach based on ellipsoidal fuzzy … …American Journal of Applied Sciences, Vol. 9, Issue 12 · 6 Dec 2012 · 6,770 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Fuzzy Neural Approach for Medical Image RetrievalC. Sriramakrishnan, A. ShanmugamImage retrieval based on a query image is necessary for effective and efficient use the information that is stored in medical image databases. Medical Image Retrieval is difficult as not only the localization and directionality of human visual system is to be considered but also the pathological condition. Image identification and segmentation for feature extraction pose a challenge to image … …Journal of Computer Science, Vol. 8, Issue 11 · 8 Sep 2012 · 6,666 accessesView PDFCite
Research ArticleOpen accessIn the abstractFuzzy Time Series: An Application to Tourism Demand ForecastingMuhammad Hisyam Lee, Maria Elena Nor, Suhartono et al.Problem statement: Forecasting is very important in many types of organizations since predictions of future events must be incorporated into the decision-making process. In the case of tourism demand, better forecast would help directors and investors make operational, tactical and strategic decisions. Besides that, government bodies need accurate tourism demand forecasts to plan required tourism infrastructures, such as accommodation site … …American Journal of Applied Sciences, Vol. 9, Issue 1 · 24 Nov 2011 · 8,474 accessesView PDFCite
Research ArticleOpen accessIn the abstractApplication of Neuro-Fuzzy Techniques for Solar RadiationW. A. Rahoma, U. Ali Rahoma, A. H. HassanProblem statement: The prediction is very useful in solar energy applications because it permits to estimate solar data for locations where measurements are not available. The developed artificial intelligence models predict the solar radiation time series more effectively compared to the conventional procedures based on the clearness index. Approach: The forecasting ability of some models could be further enhanced with … …Journal of Computer Science, Vol. 7, Issue 10 · 11 Aug 2011 · 7,077 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Hybrid Approach based on Winter's Model and Weighted Fuzzy Time Series for Forecasting Trend and Seasonal DataSuhartono, Muhammad Hisyam LeeProblem statement: In the literature, the most studied of fuzzy time series for the purpose of forecasting is the first order fuzzy time series model. In this model, only the first lagged variable is used when constructing the first order fuzzy time series model. Therefore, such approaches fail to analyze accurately trend and seasonal time series which is an important … …Journal of Mathematics and Statistics, Vol. 7, Issue 3 · 18 Jul 2011 · 7,942 accessesView PDFCite