Artificial intelligence in mixed models of systems

Code

Z2-3364 (C) - included in ARIS records

Head

PhD Aleš Belič

Period

7/1/2001 - 12/31/2003

Range in 2003

0.24 FTE

Science

Engineering sciences and technologies (3)

Reseacher status

Researcher (3)
Junior expert or technical associate (0)

Education

Doctor's degree (3)

Sex

Man (3)

Status

Employed at RO and RRD (3)

No. of publications

100–999 (3)

Projects / Programmes source: ARIS

source: ARIS

Artificial intelligence in mixed models of systems

Research activity

Code	Science	Field	Subfield
2.06.02	Engineering sciences and technologies	Systems and cybernetics	System theory and control systems

Code	Science	Field
T121	Technological sciences	Signal processing
P175	Natural sciences and mathematics	Informatics, systems theory
P176	Natural sciences and mathematics	Artificial intelligence
B115	Biomedical sciences	Biomechanics, cybernetics

Keywords

Mathematical modelling, artificial intelligence, mixed models

Evaluation (rules)

source: COBISS

Evaluation of bibliographic research performance indicators according to ARIS methodology

Citations Citations for bibliographic records in COBIB.SI that are linked to records in citation databases

source: WoS

source: Scopus

source: COBISS

Researchers (3)

no.	Code	Name and surname	Research area	Role	Period	No. of publicationsNo. of publications
1.	15395	PhD Aleš Belič	Systems and cybernetics	Head	2002 - 2003	323
2.	20181	PhD Gregor Klančar	Systems and cybernetics	Researcher	2002 - 2003	318
3.	13565	PhD Gašper Mušič	Systems and cybernetics	Researcher	2002 - 2003	451

Organisations (1)

no.	Code	Research organisation	City	Registration number	No. of publicationsNo. of publications
1.	1538	University of Ljubljana, Faculty of Electrical Engineering	Ljubljana	1626965	27,771

Abstract

The role of mathematical modelling, supported by computer simulation, dramatically increased in the recent times what is valid for the control area as well as for the other technical and nontechnical fields. Therefore the proposed postdoc project deals with the study of artificial intelligence approaches inclusion in the classic mathematical model forms which are accepted in the particular fields of science but also in the corresponding groups of users. Such mixed models should maintain the structure transparency of conventional models but also enable simpler modifications of models in the sense of inclusion of expert knowledge, nonlinearities and time variabilities of constants etc. Obtained models are in spite of their complexity better understandable and often not far from the users way of thinking. In the project the combinations with splines, fuzzy and neural models as well as genetic algorithms will be studied. The mentioned approaches will be used as the models parts but also in the model development procedure. The usefullness of the proposed models will be illustrated by the aid of concrete examples from the fields of biopharmacy and biomechanics, while their use in control will be also discussed.