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 articleProfessional

Abstract

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
Pages181-214
Number of pages34
ISBN (Electronic)978-3-319-78437-3
DOIs
Publication statusPublished - 1 Jan 2018
MoE publication typeD2 Article in professional manuals or guides or professional information systems or text book material

Publication series

NameStudies in Systems, Decision and Control
Volume140
ISSN (Print)2198-4182
ISSN (Electronic)2198-4190

Fingerprint

Complex Dynamical Systems
control process
Fuzzy neural networks
Fuzzy Neural Network
Knowledge-based
Process Control
neural network
Process control
Dynamical systems
Uncertain Data
Intuitionistic Logic
logic
Data Streams
Modeling
Instant
Fuzzy Logic
Fuzzy logic
knowledge
Neurons
Nonlinear systems

Cite this

Todorov, Y., & Terziyska, M. (2018). NEO-Fuzzy Neural Networks for Knowledge Based Modeling and Control of Complex Dynamical Systems. In V. Sgurev, V. Jotsov, & J. Kacprzyk (Eds.), Practical Issues of Intelligent Innovations (pp. 181-214). Studies in Systems, Decision and Control, Vol.. 140 https://doi.org/10.1007/978-3-319-78437-3_8
Todorov, Yancho ; Terziyska, Margarita. / NEO-Fuzzy Neural Networks for Knowledge Based Modeling and Control of Complex Dynamical Systems. Practical Issues of Intelligent Innovations. editor / Vassil Sgurev ; Vladimir Jotsov ; Janusz Kacprzyk. 2018. pp. 181-214 (Studies in Systems, Decision and Control, Vol. 140).
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Todorov, Y & Terziyska, M 2018, NEO-Fuzzy Neural Networks for Knowledge Based Modeling and Control of Complex Dynamical Systems. in V Sgurev, V Jotsov & J Kacprzyk (eds), Practical Issues of Intelligent Innovations. Studies in Systems, Decision and Control, vol. 140, pp. 181-214. https://doi.org/10.1007/978-3-319-78437-3_8

NEO-Fuzzy Neural Networks for Knowledge Based Modeling and Control of Complex Dynamical Systems. / Todorov, Yancho; Terziyska, Margarita.

Practical Issues of Intelligent Innovations. ed. / Vassil Sgurev; Vladimir Jotsov; Janusz Kacprzyk. 2018. p. 181-214 (Studies in Systems, Decision and Control, Vol. 140).

Research output: Chapter in Book/Report/Conference proceedingChapter or book articleProfessional

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Todorov Y, Terziyska M. NEO-Fuzzy Neural Networks for Knowledge Based Modeling and Control of Complex Dynamical Systems. In Sgurev V, Jotsov V, Kacprzyk J, editors, Practical Issues of Intelligent Innovations. 2018. p. 181-214. (Studies in Systems, Decision and Control, Vol. 140). https://doi.org/10.1007/978-3-319-78437-3_8