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

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu

Standard

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

Studies in Systems Decision and Control: Practical Issues of Intelligent Innovations. toim. / Vasil Sgurev; Janusz Kacprzyk. Vuosikerta 140 2018. s. 181-214 (Studies in Systems, Decision and Control; Vuosikerta 140).

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu

Harvard

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

APA

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

Vancouver

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

Author

Todorov, Yancho ; Terziyska, Margarita. / NEO-Fuzzy Neural Networks for Knowledge Based Modeling and Control of Complex Dynamical Systems. Studies in Systems Decision and Control: Practical Issues of Intelligent Innovations. Toimittaja / Vasil Sgurev ; Janusz Kacprzyk. Vuosikerta 140 2018. Sivut 181-214 (Studies in Systems, Decision and Control).

Bibtex - Lataa

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title = "NEO-Fuzzy Neural Networks for Knowledge Based Modeling and Control of Complex Dynamical Systems",
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.",
author = "Yancho Todorov and Margarita Terziyska",
year = "2018",
month = "7",
day = "26",
doi = "10.1007/978-3-319-78437-3_8",
language = "English",
isbn = "978-3-319-78436-6",
volume = "140",
series = "Studies in Systems, Decision and Control",
pages = "181--214",
editor = "Vasil Sgurev and Janusz Kacprzyk",
booktitle = "Studies in Systems Decision and Control",

}

RIS - Lataa

TY - CHAP

T1 - NEO-Fuzzy Neural Networks for Knowledge Based Modeling and Control of Complex Dynamical Systems

AU - Todorov, Yancho

AU - Terziyska, Margarita

PY - 2018/7/26

Y1 - 2018/7/26

N2 - 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.

AB - 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.

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DO - 10.1007/978-3-319-78437-3_8

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SN - 978-3-319-78436-6

VL - 140

T3 - Studies in Systems, Decision and Control

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BT - Studies in Systems Decision and Control

A2 - Sgurev, Vasil

A2 - Kacprzyk, Janusz

ER -

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