๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

A polynomial fuzzy neural network for modeling and control

โœ Scribed by Sungshin Kim; Man Hyung Lee

Publisher
Springer Japan
Year
2000
Tongue
English
Weight
374 KB
Volume
4
Category
Article
ISSN
1433-5298

No coin nor oath required. For personal study only.

๐Ÿ“œ SIMILAR VOLUMES

New fuzzy wavelet network for modeling a
New fuzzy wavelet network for modeling and control: The modeling approach
โœ Afrooz Ebadat; Navid Noroozi; Ali Akbar Safavi; Seyyed Hossein Mousavi ๐Ÿ“‚ Article ๐Ÿ“… 2011 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 949 KB

In this paper, a fuzzy wavelet network is proposed to approximate arbitrary nonlinear functions based on the theory of multiresolution analysis (MRA) of wavelet transform and fuzzy concepts. The presented network combines TSK fuzzy models with wavelet transform and ROLS learning algorithm while stil

Fuzzy neural network models for liquefac
Fuzzy neural network models for liquefaction prediction
โœ M.S Rahman; Jun Wang ๐Ÿ“‚ Article ๐Ÿ“… 2002 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 243 KB

Integrated fuzzy neural network models are developed for the assessment of liquefaction potential of a site. The models are trained with large databases of liquefaction case histories. A two-stage training algorithm is used to develop a fuzzy neural network model. In the preliminary training stage,

A fuzzy neural network and its applicati
A fuzzy neural network and its application to controls
โœ Sun Zengqi; Deng Zhidong ๐Ÿ“‚ Article ๐Ÿ“… 1996 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 503 KB

A fuzzy neural network is presented. The network is composed of two parts: an antecedent network and a consequent network. The network acts as a fuzzy logic controller. The antecedent network matches the premises of the fuzzy rules and the consequent network implements the consequences of the rules.