Intelligent forecasting: Fuzzy/neuro-fuzzy methodologies with case studies

• Autorzy: Tzafestas S. G. , Tzafestas E. S. , Maragos P.
• Języki publikacji: EN

Abstrakty

EN

A key component of the operation and planning activities in the majority of industrial plants such as electric utilities, refineries, and manufacturers, as well as enterprises and organizations is demand, or sales, forecasting. The aim of this paper is to present the solution of the forecasting problem through the application of the fuzzy/neurofuzzy methodologies. In particular, two very effective techniques are discussed based on the Takagi-Sugeno (functional reasoning) fuzzy model. The first uses the orthogonal least squares (OLS) technique to identify the structure of the fuzzy system (input variables selection and input space partitioning), and the second one uses the adaptive resonance theory (ART) technique for the same purpose. A power load forecast and a refinery product quality forecast case study are included to demonstrate the high accuracy achieved by fuzzy/neurofuzzy forecasting methods.

Słowa kluczowe

EN

fuzzy sets; neural networks; forecasting; genetic algorithms; intelligent control

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Systems Science
• Rocznik: 2000
• Tom: Vol. 26, nr 4
• Strony: 39--48
• Opis fizyczny: Bibliogr. 15 poz.

Twórcy

autor

Tzafestas S. G.

autor

Tzafestas E. S.

autor

Maragos P.

• Institute of Communication and Computer Systems, Department of Electrical and Computer Engineering, National. Technical University of Athens, Zographou, Athens, GR 15 773, Greece