2020 (vol. 30) - Number 4

*A.B. Malinowska, R. Kamocki, R. Almeida, T. Odzijewicz:*

On the existence of optimal consensus control for the fractional Cucker--Smale model

*R. Grymin, W. Bożejko, J. Pempera, M. Wodecki, Z. Chaczko:*

Algorithm for solving the Discrete-Continuous Inspection Problem

*T. Kaczorek, A. Ruszewski:*

Global stability of discrete-time nonlinear systems with descriptor standard and fractional positive linear parts and scalar feedbacks

*R. Arora, K. Gupta:*

Fuzzy goal programming technique for multi-objective indefinite quadratic bilevel programming problem

*P. Nowak, J. Czeczot, P. Grelewicz:*

Tuning rules for industrial use of the second-order Reduced Active Disturbance Rejection Controller

*M.I. Gomoyunov:*

Optimal control problems with a fixed terminal time in linear fractional-order systems

*Muhafzan, A. Nazra, L. Yulianti, Zulakmal, R. Revina:*

On LQ optimization problem subject to fractional order irregular singular systems

*M. Blizorukova, V. Maksimov:*

On one algorithm for reconstruction of an disturbance in a linear system of ordinary differential equations

ACS Abstract:

**2002 (Volume 12)**

Number 4

**Interval mathematics for analysis of multi-level granularity**

Vladik Kreinovich(University of Texas, USA) | Richard Aló(University of Houston-Downtown, USA) |

**keywords:** interval mathematics, multi-resolution granulation, space division methods, multi-D generalisations.

**Effect of fuzzy discretization in fuzzy rule-based systems for classification problems with continuous attributes**

Hisao Ishibuchi and Takashi Yamamoto(Osaka Prefecture University, Japan) |

*young, middle-aged,*and

*old*) for dividing our ages into some categories with fuzzy boundaries. In this paper, we examine the effect of fuzzy discretization on the classification performance of fuzzy rule-based systems through computer simulations on simple numerical examples and real-world pattern classification problems. For executing such computer simulations, we introduce a control parameter that specifies the overlap grade between adjacent antecedent fuzzy sets (i.e., linguistic terms) in fuzzy discretization. Interval discretization can be viewed as a special case of fuzzy discretization with no overlap. Computer simulations are performed using fuzzy discretization with various specifications of the overlap grade. Simulation results show that fuzzy rules have high generalization ability even when the domain interval of each continuous attribute is homogeneously partitioned into linguistic terms. On the other hand, generalization ability of rule-based systems strongly depends on the choice of threshold values in the case of interval discretization.

**keywords:** pattern classification, discretization of continuous attributes, rule extraction, data mining, fuzzy rules, rule weights.

**An Algorithm of granulation on numeric attributes for association rules mining**

Been-Chian Chien, Zin-Long Lin, Yi-Xue Chen(I-Shou University, Taiwan) | Tzung-Pei Hong(National University of Kaohsiung, Taiwan) |

**keywords:** data analysis, information granulation, data mining, fuzzy association rule, clustering.

**Generation of interpretable fuzzy granules by a double-clustering technique**

Giovanna Castellano, Anna Maria Fanelli and Corrado Mencar(University of Bari, Italy) |

**keywords:** information granulation, fuzzy clustering, hierarchical clustering, fuzzy rule-based model.

**Granulating XML information**

Ernesto Damiani(Universita di Milano, Italy) | Rajiv Khosla(LaTrobe University, Australia) |

**keywords:** XML information granulation, multi-sorted graphs, graph granulation, multiple levels of detail.

**Granular computing as an abstraction of data aggregation - a view on optical music recognition**

Władysław Homenda(Warsaw University of Technology, Poland) |

**keywords:** data aggregation, data abstraction, granular computing, information granules, knowledge representation, music notation, music recognition, music representation, user interface design.

**Granular Entropy and Granulation Process**

Alexander Rybalov(Jerusalem College of Technology, Israel) |

**keywords:** granulation, entropy, clustering, control, t-norm, uni-norm operators.

**Data granulation through optimization of similarity measure**

Andrzej Bargiela(The Nottingham Trent University, UK) | Witold Pedrycz(University of Alberta, Canada) | Kaoru Hirota(Tokyo Institute of Technology, Japan) |

**keywords:** logic-based clustering, information granulation, t- and s-norms, similarity index, granular prototypes, relevance, data mining, direct and inverse matching problem.

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