Search Results - subject:"fuzzy models"

Electricity demand forecasting using fuzzy hybrid intelligence-based seasonal models

Khashei, Mehdi; Chahkoutahi, Fatemeh - In: Journal of modelling in management 17 (2022) 1, pp. 154-176

Persistent link: https://www.econbiz.de/10012886288

ИВАНОВИЧ, КУДИНОВ ЮРИЙ; … - In: Проблемы управления (2008) 3, pp. 2-10

Рассмотрены основные методы нечеткой кластеризации и их применение для структурной идентификации кусочно-линейных функций и нечетких моделей с тремя видами...

Persistent link: https://www.econbiz.de/10011238048

Comparison of Two Different Adaptive Neuro-Fuzzy Inference Systems in Modelling Daily Reference Evapotranspiration

Kisi, Ozgur; Zounemat-Kermani, Mohammad - In: Water Resources Management 28 (2014) 9, pp. 2655-2675

wind speed from Adana Station, Turkey were used as inputs to the fuzzy models to estimate daily ET <Subscript> 0 … variable on FAO 56 PM ET <Subscript> 0 </Subscript> was investigated by using fuzzy models. Wind speed was found to be the most … training, validation and test accuracy of the neuro-fuzzy models was examined. It was found that the ANFIS-GP model was not …

Persistent link: https://www.econbiz.de/10010997806

L2–L∞ fuzzy control for Markov jump systems with neutral time-delays

He, Shuping; Liu, Fei - In: Mathematics and Computers in Simulation (MATCOM) 92 (2013) C, pp. 1-13

means of Takagi–Sugeno fuzzy models, the fuzzy controller systems and the overall closed-loop fuzzy dynamics are constructed …

Persistent link: https://www.econbiz.de/10011050229

Fuzzy Autoregressive Rules: Towards Linguistic Time Series Modeling

Aznarte, Jose Luis; Alcala-Fdez, Jesus; Arauzo, Antonio; … - In: Econometric Reviews 30 (2011) 6, pp. 646-668

Fuzzy rule-based models, a key element in soft computing (SC), have arisen as an alternative for time series analysis and modeling. One difference with preexisting models is their interpretability in terms of human language. Their interactions with other components have also contributed to a...

Persistent link: https://www.econbiz.de/10009228530

Using fuzzy models to migrate from customer relationship management (CRM) to customer experience management (CEM)

Gil-Lafuente, Dr. Anna Maria; Luis-Bassa, Carolina - In: Far East Journal of Psychology and Business 2 No 3 Paper 1 March (2011) 1, pp. 1-22

Relationship Marketing has made rapid progress during the last ten years. Since the development of the customer-centric model, reinforced by the emergence of CRM (Customer Relationship Management) strategies, companies have focused on finding models and tools that allow them to get to know...

Persistent link: https://www.econbiz.de/10009365830

Eliciting Expert Knowledge for Fuzzy Evaluation of Agricultural Production Systems

Kaymak, Uzay; Cornelissen, A.M.G.; van den Berg, van … - Erasmus Research Institute of Management (ERIM), … - 2002

incompatibility, information obtained at production system level is interpreted at society level in linguistic terms. Fuzzy models …

Persistent link: https://www.econbiz.de/10010731500

Eliciting Expert Knowledge for Fuzzy Evaluation of Agricultural Production Systems

Kaymak, Uzay; Cornelissen, A.M.G.; Berg, J. van den; … - Erasmus Research Institute of Management (ERIM), ERIM … - 2002

incompatibility, information obtained at production system level is interpreted at society level in linguistic terms. Fuzzy models …

Persistent link: https://www.econbiz.de/10005288415

Taking decisions by using fuzzy models

Gherasim, Ovidiu; Dornescu, Ioana - In: THE YEARBOOK OF THE “GH. ZANE” INSTITUTE OF … 18 (2009), pp. 5-14

In the most specialty papers, a mathematical model for establishing the best decisional alternatives is a couple M=(R, K) made up of a matrix R with m lines and n columns and a column vector K with m components. The mathematical modeling of an uncertain information evaluates its most probable...

Persistent link: https://www.econbiz.de/10008624638

Fuzzy models for precision measurements

Viertl, Reinhard - In: Mathematics and Computers in Simulation (MATCOM) 79 (2008) 4, pp. 874-878

Engineering and scientific measurement results of continuous quantities are not precise numbers but more or less non-precise. Therefore so-called non-precise numbers are necessary to describe measurement results. Non-precise numbers are special fuzzy subsets of the set of real numbers. For...

Persistent link: https://www.econbiz.de/10010748674