Generating dynamic fuzzy models for prediction problems

In this paper we present a new method to generate interpretable fuzzy systems from training data. A fuzzy system is developed for nonlinear systems modeling and for system state forecasting. The antecedent partition uses triangular sets with 0.5 interpolations avoiding the presence of complex overla...

Full description

Autores:

Tipo de recurso:

Fecha de publicación:

2009

Institución:

Universidad Tecnológica de Bolívar

Repositorio:

Repositorio Institucional UTB

Idioma:

eng

OAI Identifier:

oai:repositorio.utb.edu.co:20.500.12585/9125

Acceso en línea:

https://hdl.handle.net/20.500.12585/9125

Palabra clave:

Dynamic systems
Fuzzy identification
Interpretability
Least squares method
Bench-mark problems
Dynamic systems
Fuzzy identification
Fuzzy literature
Fuzzy models
Input-output
Interpretability
Least square methods
Least squares method
NARMAX model
Prediction problem
System state
Time series forecasting
Training data
Triangular sets
Composite structures
Data processing
Dynamic programming
Fuzzy systems
Hybrid systems
Nonlinear systems
Time series
Fuzzy logic

Rights

restrictedAccess

License

http://creativecommons.org/licenses/by-nc-nd/4.0/