NEO-Fuzzy Neural Networks for Knowledge Based Modeling and Control of Complex Dynamical Systems

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu

Tutkijat

  • Yancho Todorov
  • Margarita Terziyska

Organisaatiot

  • University of Food Technologies-Plovdiv

Kuvaus

Capturing the dynamics and control of fast complex nonlinear systems often requires the application of computationally efficient modeling structures in order to track the system behavior without loss of accuracy and to provide reliable predictions on purpose to process control. An available approach is to employ fuzzy-neural networks, whose abilities to handle dynamical data streams and to build rule-based relationships makes them a flexible solution. A major drawback of the classical fuzzy-neural networks is the large number of parameters associated with the rules premises and consequents parts, which need to be adapted at each discrete time instant. Therefore, in this chapter several structures with reduced number of parameters lying in the framework of a NEO-Fuzzy neuron are proposed. To increase the robustness of the models when addressing to uncommon/uncertain data variations, Type-2 and Intuitionistic fuzzy logic are introduced. An approach to design a simple NEO-Fuzzy state-space predictive controller shows the potential applicability of the proposed models for process control.

Yksityiskohdat

AlkuperäiskieliEnglanti
OtsikkoStudies in Systems Decision and Control
AlaotsikkoPractical Issues of Intelligent Innovations
ToimittajatVasil Sgurev, Janusz Kacprzyk
TilaJulkaistu - 26 heinäkuuta 2018
OKM-julkaisutyyppiA3 Kirjan osa tai toinen tutkimuskirja

Julkaisusarja

NimiStudies in Systems, Decision and Control
Vuosikerta140
ISSN (painettu)2198-4182
ISSN (elektroninen)2198-4190

ID: 26774444