@article{MTMT:2235769,
	title = {SVD-based reduction to MISO TS models},
	url = {https://m2.mtmt.hu/api/publication/2235769},
	author = {Baranyi, Péter Zoltán and Y, Yam and Várkonyiné Kóczy, Annamária and R J, Patton},
	doi = {10.1109/TIE.2002.807673},
	journal-iso = {IEEE T IND ELECTRON},
	journal = {IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS},
	volume = {50},
	unique-id = {2235769},
	issn = {0278-0046},
	keywords = {Mathematical models; Computational complexity; Approximation theory; fuzzy control; complexity reduction; Fuzzy sets; Takagi-Sugeno fuzzy models; Singular-value-decomposition; SVD-based fuzzy rule base reduction; Higher order singular value decomposition (SVD)},
	year = {2003},
	eissn = {1557-9948},
	pages = {232-242},
	orcid-numbers = {Baranyi, Péter Zoltán/0000-0002-8265-5849; Várkonyiné Kóczy, Annamária/0000-0002-6932-8608}
}

@article{MTMT:2235770,
	title = {Adaptation of SVD Based Fuzzy Reduction via Minimal Expansion},
	url = {https://m2.mtmt.hu/api/publication/2235770},
	author = {Baranyi, Péter Zoltán and Várkonyiné Kóczy, Annamária},
	doi = {10.1109/19.997816},
	journal-iso = {IEEE T INSTRUM MEAS},
	journal = {IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT},
	volume = {51},
	unique-id = {2235770},
	issn = {0018-9456},
	abstract = {Most adopted fuzzy inference techniques do not hold the universal approximation property if the numbers of antecedent sets are limited. This fact and the exponential complexity problem of widely adopted fuzzy logic techniques show the contradictory features of fuzzy rule bases in pursuit of good approximation. As a result, complexity reduction emerged in fuzzy theory. The natural disadvantage of using complexity reduction is that the adaptivity property of the reduced approximation becomes highly restricted. This paper proposes a technique for singular value decomposition (SVD) based reduction developed in [1], which may alleviate the adaptivity restriction.},
	keywords = {Computational complexity; Approximation theory; Fuzzy sets; Knowledge based systems; Theorem proving; Tensors; Singular value decomposition; Single value decomposition (SVD); Rule-base complexity reduction; Higher-order tensor decomposition},
	year = {2002},
	eissn = {1557-9662},
	pages = {222-226},
	orcid-numbers = {Baranyi, Péter Zoltán/0000-0002-8265-5849; Várkonyiné Kóczy, Annamária/0000-0002-6932-8608}
}

@article{MTMT:2235743,
	title = {SVD-based Complexity Reduction to TS Fuzzy Models},
	url = {https://m2.mtmt.hu/api/publication/2235743},
	author = {Baranyi, Péter Zoltán and Yam, Y and Várkonyiné Kóczy, Annamária and Patton, RJ and Michelberger, Pál and Sugiyama, M},
	doi = {10.1109/41.993277},
	journal-iso = {IEEE T IND ELECTRON},
	journal = {IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS},
	volume = {49},
	unique-id = {2235743},
	issn = {0278-0046},
	abstract = {One of the typical important criteria to be considered in real-time control applications Is the computational complexity of the controllers, observers, and models applied. In this paper, a singular value decomposition (SVD)-based complexity reduction technique Is proposed for Takagi Sugeno (TS) fuzzy models. The main motivation is that the TS fuzzy model has exponentially growing computational complexity with the improvement of its approximation property through, as usually practiced, increasing the density of antecedent terms. The reduction technique proposed here Is capable of defining the contribution of each local linear model included in the TS fuzzy model, which serves to remove the weakly contributing ones as according to a given threshold. Reducing the number of models leads directly to the computational complexity reduction. This work also includes a number of numerical and application examples.},
	keywords = {Computational complexity; fuzzy control; complexity reduction; Real time systems; Induction motors; fuzzy rule base reduction; Singular value decomposition (SVD); Pulse width modulation; Anytime systems; TS fuzzy model},
	year = {2002},
	eissn = {1557-9948},
	pages = {433-443},
	orcid-numbers = {Baranyi, Péter Zoltán/0000-0002-8265-5849; Várkonyiné Kóczy, Annamária/0000-0002-6932-8608}
}

@article{MTMT:2625220,
	title = {Reduction of Fuzzy Rule Base Via Singular Value Decomposition},
	url = {https://m2.mtmt.hu/api/publication/2625220},
	author = {Y, Yam and Baranyi, Péter Zoltán and C T, Yang},
	doi = {10.1109/91.755394},
	journal-iso = {IEEE T FUZZY SYST},
	journal = {IEEE TRANSACTIONS ON FUZZY SYSTEMS},
	volume = {7},
	unique-id = {2625220},
	issn = {1063-6706},
	keywords = {Mathematical models; Algorithms; Membership functions; Fuzzy sets; Knowledge based systems; Error analysis; Singular value decomposition; Statistical methods; Takagi-Sugeno-Kang (TSK) model; Reduced order systems},
	year = {1999},
	eissn = {1941-0034},
	pages = {120-132},
	orcid-numbers = {Baranyi, Péter Zoltán/0000-0002-8265-5849}
}

@inproceedings{MTMT:1053045,
	title = {Application of the approximate fuzzy reasoning based on interpolation in the vague environment of the fuzzy rulebase in the fuzzy logic controlled path tracking strategy of differential steered AGVS},
	url = {https://m2.mtmt.hu/api/publication/1053045},
	author = {Kovács, Szilveszter and Kóczy, T. László},
	booktitle = {Computational Intelligence Theory and Applications},
	doi = {10.1007/3-540-62868-1_138},
	unique-id = {1053045},
	year = {1997},
	pages = {456-467},
	orcid-numbers = {Kovács, Szilveszter/0000-0001-7942-7290; Kóczy, T. László/0000-0003-1316-4832}
}

@article{MTMT:1046191,
	title = {Size Reduction by Interpolation in Fuzzy Rule Bases},
	url = {https://m2.mtmt.hu/api/publication/1046191},
	author = {Kóczy, T. László and K, Hirota},
	doi = {10.1109/3477.552182},
	journal-iso = {IEEE T SYST MAN CY B},
	journal = {IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS},
	volume = {27},
	unique-id = {1046191},
	issn = {1083-4419},
	abstract = {Fuzzy control is at present still the most important area of real applications for fuzzy theory. It is a generalized form of expert control using fuzzy sets in the definition of vague/linguistic predicates, modelling a system by If... then rules. In the classical approaches it is necessary that observations on the actual state of the system partly match (fire) one or several rules in the model (fired rules), and the conclusion is calculated by the evaluation of the degrees of matching and the fired rules. Interpolation helps reducing the complexity as it allows rule bases with gaps, Various interpolation approaches are shown. It is proposed that dense rule bases should be reduced so that only the minimal necessary number of rules remain still containing the essential information in the original base, and all other rules are replaced by the interpolation algorithm that however can recover them with a certain accuracy prescribed before reduction. The interpolation method used for demonstration is the Lagrange-method supplying the best fitting minimal degree polynomial, The paper concentrates on the reduction technique that is rather independent from the style of the interpolation model, but cannot be given in the form of a tractable algorithm. An example is shown to illustrate possible results and difficulties with the method.},
	year = {1997},
	pages = {14-25},
	orcid-numbers = {Kóczy, T. László/0000-0003-1316-4832}
}