Krzysztof Wiktorowicz

Hybridization of Fuzzy Sets and Machine Learning Models for Solving Regression and Classification Problems

Krzysztof Wiktorowicz

This paper presents a novel approach to hybridizing fuzzy sets with machine learning models. The proposed hybrid system is not based on fuzzy rules, so the curse of dimensionality that appears in fuzzy rule-based systems is avoided. The system consists of a …

Hybridization of Fuzzy Sets and Machine Learning Models for Solving Regression and Classification Problems

Krzysztof Wiktorowicz

Interpretable fuzzy model reference adaptive controller for linear MIMO systems: MATLAB implementation and simulation toolkit

Krzysztof Wiktorowicz, Jacek Kluska

We present a MATLAB-based software package for designing and simulating an interpretable fuzzy model reference adaptive controller (FMRAC) for linear multi-input-multi-output (MIMO) systems with partially known dynamics. The toolkit implements a step-by-step procedure for synthesizing a fuzzy controller that guarantees closed-loop stability …

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Model reference adaptive controller with interpretable fuzzy rules for linear MIMO systems

Jacek Kluska, Krzysztof Wiktorowicz

This paper presents a method for fuzzy model reference adaptive control system design for a partially known multi-input multi-output (MIMO) linear dynamic plant. Differential or convolutional equations can describe the plant to be controlled, and the fuzzy controller is assumed to be …

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T2RFIS: type-2 regression-based fuzzy inference system

Krzysztof Wiktorowicz

Abstract This article discusses a novel type-2 fuzzy inference system with multiple variables in which no fuzzy rules are explicitly defined. By using a rule-free system, we avoid the serious disadvantage of rule-based systems, which are burdened with the curse of dimensionality. …

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RFIS: regression-based fuzzy inference system

Krzysztof Wiktorowicz

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Correction to: Identification of time series models using sparse Takagi–Sugeno fuzzy systems with reduced structure

Krzysztof Wiktorowicz, Tomasz Krzeszowski

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Identification of time series models using sparse Takagi–Sugeno fuzzy systems with reduced structure

Krzysztof Wiktorowicz, Tomasz Krzeszowski

Abstract Simplifying fuzzy models, including those for predicting time series, is an important issue in terms of their interpretation and implementation. This simplification can involve both the number of inference rules (i.e., structure) and the number of parameters. This paper proposes novel …

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Training Sparse Fuzzy Classifiers Using Metaheuristic Optimization

Tomasz Krzeszowski, Krzysztof Wiktorowicz

This paper proposes a novel classification model called sparse fuzzy classifier (SFC), which uses a sparse Takagi-Sugeno fuzzy system to classify data. This system is considered with Gaussian fuzzy sets in the antecedents and first-order polynomials in the consequents of fuzzy rules. …

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Combined Regularized Discriminant Analysis and Swarm Intelligence Techniques for Gait Recognition

Tomasz Krzeszowski, Krzysztof Wiktorowicz

In the gait recognition problem, most studies are devoted to developing gait descriptors rather than introducing new classification methods. This paper proposes hybrid methods that combine regularized discriminant analysis (RDA) and swarm intelligence techniques for gait recognition. The purpose of this study …

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Approximation of two-variable functions using high-order Takagi–Sugeno fuzzy systems, sparse regressions, and metaheuristic optimization

Krzysztof Wiktorowicz, Tomasz Krzeszowski

Abstract This paper proposes a new hybrid method for training high-order Takagi–Sugeno fuzzy systems using sparse regressions and metaheuristic optimization. The fuzzy system is considered with Gaussian fuzzy sets in the antecedents and high-order polynomials in the consequents of fuzzy rules. The …

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Correction to: Sparse regressions and particle swarm optimization in training high-order Takagi–Sugeno fuzzy systems

Krzysztof Wiktorowicz, Tomasz Krzeszowski, Krzysztof Przednówek

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