Publikacje w roku 2012

Artykuły w czasopismach z listy A MNiSW

[1]Bednarczuk E., Kruger A.: Error bounds for vector-valued functions:Necessary and sufficient conditions. NONLINEAR ANALYSIS-THEORY METHODS & APPLICATIONS, vol. 75, No. 1, 2012, ss. 1124-1140, 37 poz. bibl.

[2]Błaszczyński J., Greco S., Słowiński R.: Inductive discovery of laws using monotonic rules. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, vol. 25, No. 2, 2012, ss. 284-294.

[3]Breve F., Zhang L., Quiles M., Pedrycz W., Liu J.: Particle competition and cooperation in networks for semi-supervised learning. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, vol. 24, No. 9, 2012, ss. 1686-1698, 55 poz. bibl.

[4]Brezhneva O., Tretiakow A.: An elementary proof of the Lagrange multipler theorem in normed linear spaces. OPTIMIZATION, vol. 61, No. 12, 2012, ss. 1511-1517.

[5]Brezhneva O., Tretiakow A., Wright S.: A short elementary proof of the Lagrange multiplier theorem. OPTIMIZATION LETTERS, vol. 6, No. 8, 2012, ss. 1597-1601.

[6]Corrente S., Greco S., Słowiński R.: Multiple Criteria Hierarchy Process in Robust Ordinal Regression. DECISION SUPPORT SYSTEMS, vol. 53, No. 3, 2012, ss. 660-674.

[7]Evtushenko Y., Tretiakow A.: Elementary Proof of Constructive Versions of the Tangent Direction Theorem and the Implicit Function Theorem. DOKLADY MATHEMATICS, vol. 85, No. 1, 2012, ss. 23-28.

[8]Ganzha M., Omelczuk A., Paprzycki M., Wypysiak M.: Information resource management in an agent-based virtual organization—initial implementation. COMPUTER SCIENCE AND INFORMATION SYSTEMS, vol. 9, No. 3, 2012, ss. 1307-1330.

[9]Gągolewski M.: On the Relationship Between Symmetric Maxitive, Minitive, and Modular Aggregation Operators. INFORMATION SCIENCES, vol. 221, 2013 [druk w 2012 roku], ss. 170-180, 25 poz. bibl.

[10]Gągolewski M., Mesiar R.: Aggregating Different Paper Quality Measures with a Generalized h-index. JOURNAL OF INFORMETRICS, vol. 6, No. 4, 2012, ss. 566-579, 39 poz. bibl.

[11]Greco S., Kadziński M., Mousseau V., Słowiński R.: Robust ordinal regression for multiple criteria group decision: UTAGMS-GROUP and UTADISGMS-GROUP. DECISION SUPPORT SYSTEMS, vol. 52, No.  3, 2012, ss. 549-561.

[12]Greco S., Matarazzo B., Słowiński R.: The bipolar complemented de Morgan Brouwer-Zadeh distributive lattice as an algebraic structure for the Dominance-based Rough Set Approach. FUNDAMENTA INFORMATICAE, vol. 115, No. 1, 2012, ss. 25-56.

[13]Greco S., Słowiński R., Szczęch I.: Properties of rule interestingness measures and alternative approaches to normalization of measures. INFORMATION SCIENCES, vol. 216, 2012, ss. 1-16.

[14]Grzegorzewski P.: Fuzzy number approximation via shadowed sets. INFORMATION SCIENCES, vol. 225, 2013 [druk w 2012 roku], ss. 35-46, 30 poz. bibl.

[15]Hołubiec J., Szkatuła G., Wagner D.: A knowledge-based model of parliamentary election. INFORMATION SCIENCES, vol. 202, 2012, ss. 24-40, 20 poz. bibl.

[16]Ignatova M., Kukavica I., Lasiecka I., Tuffaha A.: On well-posedness for a free boundary fluid-structure model. JOURNAL OF MATHEMATICAL PHYSICS, vol. 53, No. 11, 2012.

[17]Kadziński M., Greco S., Słowiński R.: Extreme ranking analysis in robust ordinal regression. OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE, No. 40, 2012, ss. 488-501.

[18]Kadziński M., Greco S., Słowiński R.: Selection of a representative set of parameters for robust ordinal regression outranking methods. COMPUTERS & OPERATIONS RESEARCH, vol. 39, 2012, ss. 2500-2519.

[19]Kadziński M., Greco S., Słowiński R.: Selection of a representative value function in robust multiple criteria ranking and choice. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, vol. 217, No. 3, 2012, ss. 541-553.

[20]Kadziński M., Słowiński R.: Interactive robust cone contraction method for multiple objective optimization problems. INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING, vol. 11, No. 2, 2012, ss. 327-357.

[21]Kaltenbacher B., Lasiecka I., Pospieszalska M.: Well-posedness and exponential decay of the energy in the nonlinear Jordan-Moore-Gibson Thompson equation arising in high intensity ultrasound. MATHEMATICAL MODELS & METHODS IN APPLIED SCIENCES, vol. 22, No. 11, 2012, ss. 1-34.

[22]Kowalewski A., Lasiecka I., Sokołowski J.: Sensitivity analysis of hyperbolic optimal control problems. COMPUTATIONAL OPTIMIZATION AND APPLICATIONS, vol. 52, No. 1, 2012, ss. 147-179.

[23]Kulczycki P., Charytanowicz M., Kowalski P., Łukasik S.: The Complete Gradient Clustering Algorithm: properties in practical applications. JOURNAL OF APPLIED STATISTICS, vol. 39, No. 6, 2012, ss. 1211-1224, 33 poz. bibl.

[24]Lasiecka I., McDevitt T., Marchand R.: Boundary Control and Hidden Trace Regularity of a Semigroup Associated with a Beam Equation and Non-Dissipative Boundary Conditions. DYNAMIC SYSTEMS AND APPLICATIONS, vol. 21, 2012, ss. 467-490.

[25]Lee M., Pedrycz W.: Adaptive learning of ordinal-numerical mappings through fuzzy clustering for the objects of mixed features. FUZZY SETS AND SYSTEMS, vol. 161, No. 4, 2010 [druk w 2012 roku], ss. 564-577, 32 poz. bibl.

[26]Liang X., Pedrycz W.: Logic-based fuzzy networks: a study in system modeling with triangular norms and uninorms. FUZZY SETS AND SYSTEMS, vol. 160, No. 24, 2009 [druk w 2012 roku], ss. 3475-3502, 56 poz. bibl.

[27]Liu X., Zhai K., Pedrycz W.: An improved association rule mining method. EXPERT SYSTEMS WITH APPLICATIONS, vol. 39, 2012, ss. 1362-1374, 60 poz. bibl.

[28]Mitra S., Kundu P., Pedrycz W.: Feature selection using structural similarity. INFORMATION SCIENCES, vol. 198, 2012, ss. 48-61.

[29]Myśliński A.: Topology Optimization of Quasistatic Contact Problems. INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND COMPUTER SCIENCE, vol. 22, No. 2, 2012, ss. 269-280, 31 poz. bibl.

[30]Nam Y., Zhao Q., Cichocki A., Choi S.: Tongue-Rudder: A Glossokinetic-Potential-Based Tongue-Machine Interface. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, vol. 59, No. 1, 2012, ss. 290-299, 21 poz. bibl.

[31]Nowak P., Romaniuk M.: Pricing and simulations of catastrophe bonds. INSURANCE MATHEMATICS & ECONOMICS, vol. 52, No. 1, 2013 [druk w 2012 roku], ss. 18-28, 58 poz. bibl.

[32]Oh S., Kim W., Pedrycz W., Joo S.: Design of K-means clustering-based polynomial radial basis function neural networks (pRBFNNs) realized with the aid of particle swarm optimization and differential evolution. NEUROCOMPUTING, vol. 78, 2012, ss. 121-132.

[33]Osmolovskii N.: Second-order sufficient optimality conditions for control problems with linearly independent gradients of control constraints. ESAIM-CONTROL OPTIMISATION AND CALCULUS OF VARIATIONS , vol. 18, No. 2, 2012, ss. 452-482.

[34]Park H., Pedrycz W., Chung Y., Oh S.: Modeling of the charging characteristic of linear-type superconducting power supply using granular-based radial basis function neural networks. EXPERT SYSTEMS WITH APPLICATIONS, vol. 39, No. 1, 2012, ss. 1021-1039.

[35]Paternain D., Jurio A., Barrenechea E., Bustince H., Bedregal B., Szmidt E.: An alternative to fuzzy methods in decision-making. EXPERT SYSTEMS WITH APPLICATIONS, vol. 39, 2012, ss. 7729-7735, 34 poz. bibl.

[36]Pedrycz A., Hirota K., Pedrycz W., Dong F.: Granular representation and granular computing with fuzzy sets. FUZZY SETS AND SYSTEMS, vol. 203, 2012, ss. 17-32.

[37]Pedrycz W., Ahmad S.: Evolutionary feature selection via structure retention. EXPERT SYSTEMS WITH APPLICATIONS, vol. 39, 2012, ss. 11801-11807, 45 poz. bibl.

[38]Pedrycz W., Aliev R.: Logic-oriented neural networks for fuzzy neurocomputing. NEUROCOMPUTING, vol. 73, No. 1-3, 2009 [druk w 2012 roku], ss. 10-23, 31 poz. bibl.

[39]Pedrycz W., Bargieła A.: An optimization of allocation of information granularity in the interpretation of data structures: towards granular fuzzy clustering. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, vol. 42, No. 3, 2012, ss. 582-591, 35 poz. bibl.

[40]Pedrycz W., Russo B., Succi G.: Knowledge transfer in system modeling and its realization through an optimal allocation of information granularity. APPLIED SOFT COMPUTING, vol. 12, 2012, ss. 1985-1995, 34 poz. bibl.

[41]Pedrycz W., Song M.: A genetic reduction of feature space in the design of fuzzy models. APPLIED SOFT COMPUTING, vol. 12, 2012, ss. 2801-2816.

[42]Pedrycz W., Song M.: Granular fuzzy models: a study in knowledghe management in fuzzy modeling. INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, vol. 53, No. 7, 2012, ss. 1061-1079, 25 poz. bibl.

[43]Peregud G., Zubek J., Ganzha M., Paprzycki M.: Implementing an eXAT-based distributed monitoring system prototype. COMPUTER SCIENCE AND INFORMATION SYSTEMS, vol. 9, No. 3, 2012, ss. 1249-1285.

[44]Roh S., Ahn T., Pedrycz W.: Fuzzy linear regression based on Polynomial Neural Networks. EXPERT SYSTEMS WITH APPLICATIONS, vol. 39, 2012, ss. 8909-8928.

[45]Studziński J.: SCADA do zarządzania miejskim systemem zaopatrzenia w wodę. OCHRONA ŚRODOWISKA, No. 1, 2012, ss. 26-30, 8 poz. bibl.

[46]Wilbik A., Keller J.: A distance metric for a space of linguistic summaries. FUZZY SETS AND SYSTEMS, vol. 208, 2012, ss. 79-94, 32 poz. bibl.

[47]Wilbik A., Keller J.: A fuzzy measure similarity between sets of linguistic summaries. IEEE TRANSACTIONS ON FUZZY SYSTEMS, vol. 21, No. 1, 2012, ss. 183-189, 24 poz. bibl.

[48]Wilbik A., Keller J., Alexander G.: Similarity evaluation of sets of linguistic summaries. INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, vol. 27, No. 10, 2012, ss. 926-938, 28 poz. bibl.

[49]Zadrożny S., Kacprzyk J.: Bipolar queries: an aggregation operator focused perspective. FUZZY SETS AND SYSTEMS, vol. 196, 2012, ss. 69-81.

[50]Zadrożny S., Kacprzyk J., De Tre G.: Bipolar queries in textual information retrieval: A new perspective. INFORMATION PROCESSING & MANAGEMENT, vol. 48, No. 3, 2012, ss. 390-398.

[51]Zakrzewski L., Tretiakow A., Khulap G.: A Model of Computer Structure Organization for Solving Global Optimization Problems with an Algorithmic Complexity That is Independent of the Problem Size. JOURNAL OF COMPUTER AND SYSTEM SCIENCES, vol. 51, No. 2, 2012,

[52]Zhao J., Wang W., Pedrycz W., Tian X.: Online Parameter Optimization-Based Prediction for Converter Gas System by Parallel Strategies. IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, vol. 20, No. 3, 2012, ss. 835-845.

[53]Zhou G., Cichocki A.: Canonical Polyadic Decomposition Based on a Single Mode Blind Source Separation. IEEE SIGNAL PROCESSING LETTERS, vol. 19, No. 8, 2012, ss. 523-526.

[54]Zhou G., Cichocki A.: Fast and unique Tucker decompositions via Multiway Blind Source Separation. BULLETIN OF THE POLISH ACADEMY OF SCIENCES: TECHNICAL SCIENCES, vol. 60, No. 3, 2012, ss. 389-405.