✦ LIBER ✦

A self-similar local neuro-fuzzy model for short-term demand forecasting

✍ Scribed by Hassani, Hossein; Abdollahzadeh, Majid; Iranmanesh, Hossein; Miranian, Arash

Book ID: 121560015
Publisher: Academy of Mathematics and Systems Science, Chinese Academy of Sciences
Year: 2014
Tongue: English
Weight: 691 KB
Volume: 27
Category: Article
ISSN: 1009-6124
DOI: 10.1007/s11424-014-3299-y

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Short-term and long-term streamflow fore

Short-term and long-term streamflow forecasting using a wavelet and neuro-fuzzy conjunction model

✍ Jalal Shiri; Ozgur Kisi 📂 Article 📅 2010 🏛 Elsevier Science 🌐 English ⚖ 927 KB

A short-term, pattern-based model for wa

A short-term, pattern-based model for water-demand forecasting

✍ Alvisi, Stefano; Franchini, Marco; Marinelli, Alberto 📂 Article 📅 2007 🏛 IWA Publishing 🌐 English ⚖ 705 KB

A fully adaptive forecasting model for s

A fully adaptive forecasting model for short-term drinking water demand

✍ Bakker, M.; Vreeburg, J.H.G.; van Schagen, K.M.; Rietveld, L.C. 📂 Article 📅 2013 🏛 Elsevier Science 🌐 English ⚖ 733 KB

A new fuzzy-based feature selection and

A new fuzzy-based feature selection and hybrid TLA–ANN modelling for short-term load forecasting

✍ Kavousi-Fard, Abdollah 📂 Article 📅 2013 🏛 Taylor and Francis Group 🌐 English ⚖ 175 KB

Application of a fuzzy model for short-t

Application of a fuzzy model for short-term load forecast with group method of data handling enhancement

✍ Shyh-Jier Huang; Kuang-Rong Shih 📂 Article 📅 2002 🏛 Elsevier Science 🌐 English ⚖ 215 KB

A constrained orthogonal least-squares m

A constrained orthogonal least-squares method for generating TSK fuzzy models: Application to short-term load forecasting

✍ Paris A. Mastorocostas; John B. Theocharis; Vassilios S. Petridis 📂 Article 📅 2001 🏛 Elsevier Science 🌐 English ⚖ 237 KB

In this paper, an orthogonal least-squares (OLS) based modeling method is developed, named the constrained OLS (C-OLS), for generating simple and e cient TSK fuzzy models. The method is a two-stage model building technique, where both premise and consequent identiÿcation are simultaneously performed