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

Yancho Todorov, Margarita Terziyska

Research output: Chapter in Book/Report/Conference proceedingChapter or book articleScientificpeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Title of host publicationPractical Issues of Intelligent Innovations
EditorsVassil Sgurev, Vladimir Jotsov, Janusz Kacprzyk
Number of pages34
ISBN (Electronic)978-3-319-78437-3
ISBN (Print)978-3-319-78436-6
Publication statusPublished - 1 Jan 2018
MoE publication typeA3 Part of a book or another research book

Publication series

SeriesStudies in Systems, Decision and Control


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