System Identification Using Monotonic Fuzzy Models

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00348000" target="_blank" >RIV/68407700:21230/21:00348000 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1007/978-3-030-47124-8_19" target="_blank" >https://doi.org/10.1007/978-3-030-47124-8_19</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-47124-8_19" target="_blank" >10.1007/978-3-030-47124-8_19</a>

Result language

angličtina

Original language name

System Identification Using Monotonic Fuzzy Models

Original language description

Fuzzy models became very popular tool for identification of nonlinear systems from input-output data. Unfortunately, their behaviour is considerably deteriorated in the regions where the data is sparse or contaminated by a noise. The paper shows that when a nonlinear function describing the systems is monotonic then a fuzzy system guaranteeing monotonicity significantly improves the performance of the fuzzy model. Furthermore, sufficient conditions for monotonicity of Takagi-Sugeno fuzzy systems with Gaussian membership functions are derived.

Czech name

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Czech description

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Type

C - Chapter in a specialist book

CEP classification

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OECD FORD branch

20205 - Automation and control systems

Project

<a href="/en/project/GA18-26278S" target="_blank" >GA18-26278S: Incorporation of Prior Knowledge for Identification of Nonlinear Systems</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Book/collection name

Studies in Fuzziness and Soft Computing

ISBN

978-3-030-47124-8

Number of pages of the result

14

Pages from-to

229-242

Number of pages of the book

250

Publisher name

Springer

Place of publication

Wien

UT code for WoS chapter

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