New Approach for Interpretability of Neuro-Fuzzy Systems with Parametrized Triangular Norms
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[1] Magdalena Laskowska,et al. Mesoporous silica SBA-15 functionalized by nickel–phosphonic units: Raman and magnetic analysis , 2014 .
[2] Marcin Zalasinski,et al. On-line signature verification using vertical signature partitioning , 2014, Expert Syst. Appl..
[3] Ryszard Tadeusiewicz,et al. Artificial neural networks – a novel tool in modelling the effectiveness of growth hormone (GH) therapy in children with GH deficiency , 2015 .
[4] Yoichi Hayashi,et al. On the Strong Convergence of the Orthogonal Series-Type Kernel Regression Neural Networks in a Non-stationary Environment , 2012, ICAISC.
[5] Danuta Rutkowska,et al. Medical Diagnosis with Type-2 Fuzzy Decision Trees , 2009 .
[6] Meng Joo Er,et al. New Method for Dynamic Signature Verification Using Hybrid Partitioning , 2014, ICAISC.
[7] Marcin Korytkowski,et al. Fast image classification by boosting fuzzy classifiers , 2016, Inf. Sci..
[8] Krzysztof Cpałka,et al. A new method of on-line signature verification using a flexible fuzzy one-class classifier , 2011 .
[9] Leszek Rutkowski,et al. New method for the on-line signature verification based on horizontal partitioning , 2014, Pattern Recognit..
[10] Ryotaro Kamimura,et al. Accumulative Information Enhancement In The Self-Organizing Maps And Its Application To The Analysis Of Mission Statements , 2015, J. Artif. Intell. Soft Comput. Res..
[11] Simone A. Ludwig. Repulsive Self-Adaptive Acceleration Particle Swarm Optimization Approach , 2014, J. Artif. Intell. Soft Comput. Res..
[12] Tandra Pal,et al. Group Decision Making using Interval-Valued Intuitionistic Fuzzy Soft Matrix and Confident Weight of Experts , 2014, J. Artif. Intell. Soft Comput. Res..
[13] P. Shukla,et al. A new approach for tuning interval type-2 fuzzy knowledge bases using genetic algorithms , 2014 .
[14] Homayoun Najjaran,et al. Adaboost.MRT: Boosting regression for multivariate estimation , 2014, Artif. Intell. Res..
[15] Piotr Duda,et al. The CART decision tree for mining data streams , 2014, Inf. Sci..
[16] Ryszard Tadeusiewicz,et al. Neural network models - a novel tool for predicting the efficacy of growth hormone (GH) therapy in children with short stature. , 2015, Neuro endocrinology letters.
[17] Ryszard Tadeusiewicz,et al. Neural networks as a tool for modeling of biological systems , 2015, Bio Algorithms Med Syst..
[18] Ryszard Tadeusiewicz,et al. Neural Networks In Mining Sciences – General Overview And Some Representative Examples , 2015 .
[19] Antonio A. Márquez,et al. A multi-objective evolutionary algorithm with an interpretability improvement mechanism for linguistic fuzzy systems with adaptive defuzzification , 2010, International Conference on Fuzzy Systems.
[20] Petia D. Koprinkova-Hristova,et al. New Method for Nonlinear Fuzzy Correction Modelling of Dynamic Objects , 2014, ICAISC.
[21] Krystian Lapa,et al. A new method for designing neuro-fuzzy systems for nonlinear modelling with interpretability aspects , 2014, Neurocomputing.
[22] Meng Joo Er,et al. Online Speed Profile Generation for Industrial Machine Tool Based on Neuro-fuzzy Approach , 2010, ICAISC.
[23] Andri Riid,et al. Interpretability, Interpolation and Rule Weights in Linguistic Fuzzy Modeling , 2011, WILF.
[24] Marcin Korytkowski,et al. Application of Neural Networks in Assessing Changes around Implant after Total Hip Arthroplasty , 2012, ICAISC.
[25] Krystian Lapa,et al. A New Method for Designing and Complexity Reduction of Neuro-fuzzy Systems for Nonlinear Modelling , 2013, ICAISC.
[26] L. Rutkowski,et al. Flexible weighted neuro-fuzzy systems , 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..
[27] Eneko Osaba,et al. Golden ball: a novel meta-heuristic to solve combinatorial optimization problems based on soccer concepts , 2014, Applied Intelligence.
[28] Marcin Korytkowski,et al. Neuro-fuzzy Rough Classifier Ensemble , 2009, ICANN.
[29] Lukasz Bartczuk,et al. Type-2 Fuzzy Decision Trees , 2006, ICAISC.
[30] Kandarpa Kumar Sarma,et al. A Class of Neuro-Computational Methods for Assamese Fricative Classification , 2015, J. Artif. Intell. Soft Comput. Res..
[31] Meng Joo Er,et al. On the Application of the Parzen-Type Kernel Regression Neural Network and Order Statistics for Learning in a Non-stationary Environment , 2012, ICAISC.
[32] Wayne Nelson,et al. Analysis of Performance-Degradation Data from Accelerated Tests , 1981, IEEE Transactions on Reliability.
[33] Meng Joo Er,et al. A New Method for the Dynamic Signature Verification Based on the Stable Partitions of the Signature , 2015, ICAISC.
[34] Rafal Scherer,et al. Neuro-fuzzy Systems with Relation Matrix , 2010, ICAISC.
[35] Piotr Dziwiñski,et al. A New Algorithm for Identification of Significant Operating Points Using Swarm Intelligence , 2014, ICAISC.
[36] Vincent Vanhoucke,et al. Interpretability in Multidimensional Classification , 2003 .
[37] Leszek Rutkowski,et al. Adaptive probabilistic neural networks for pattern classification in time-varying environment , 2004, IEEE Transactions on Neural Networks.
[38] Krzysztof Cpalka,et al. A New Method for Design and Reduction of Neuro-Fuzzy Classification Systems , 2009, IEEE Transactions on Neural Networks.
[39] Krzysztof Cpalka. A Method for Designing Flexible Neuro-fuzzy Systems , 2006, ICAISC.
[40] X Li,et al. Fuzzy Regression Modeling for Tool Performance Prediction and Degradation Detection , 2010, Int. J. Neural Syst..
[41] Francisco Herrera,et al. Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures , 2011, Inf. Sci..
[42] Rafal Grycuk,et al. From Single Image to List of Objects Based on Edge and Blob Detection , 2014, ICAISC.
[43] Anna Maria Fanelli,et al. Interpretability assessment of fuzzy knowledge bases: A cointension based approach , 2011, Int. J. Approx. Reason..
[44] Francisco Herrera,et al. A Multiobjective Evolutionary Approach to Concurrently Learn Rule and Data Bases of Linguistic Fuzzy-Rule-Based Systems , 2009, IEEE Transactions on Fuzzy Systems.
[45] Rafał Scherer,et al. Relational Equations Initializing Neuro-Fuzzy System , 2002 .
[46] Giovanna Castellano,et al. On the Role of Interpretability in Fuzzy Data Mining , 2007, Int. J. Uncertain. Fuzziness Knowl. Based Syst..
[47] Marcin Korytkowski,et al. From Ensemble of Fuzzy Classifiers to Single Fuzzy Rule Base Classifier , 2006, ICAISC.
[48] Janusz T. Starczewski,et al. Learning Methods for Type-2 FLS Based on FCM , 2010, ICAISC.
[49] Yoichi Hayashi,et al. New Fast Algorithm for the Dynamic Signature Verification Using Global Features Values , 2015, ICAISC.
[50] Lukasz Laskowski,et al. Functionalization of SBA-15 mesoporous silica by Cu-phosphonate units: Probing of synthesis route , 2014 .
[51] Lipo Wang,et al. Evolutionary Approach with Multiple Quality Criteria for Controller Design , 2014, ICAISC.
[52] Hisao Ishibuchi,et al. Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning , 2007, Int. J. Approx. Reason..
[53] Lukasz Bartczuk. Gene Expression Programming in Correction Modelling of Nonlinear Dynamic Objects , 2015, ISAT.
[54] Haruhiko Nishimura,et al. Chaotic States Induced By Resetting Process In Izhikevich Neuron Model , 2015, J. Artif. Intell. Soft Comput. Res..
[55] Krystian Lapa,et al. A New Approach to Designing Interpretable Models of Dynamic Systems , 2013, ICAISC.
[56] Hisao Ishibuchi,et al. Comparison of the Michigan and Pittsburgh approaches to the design of fuzzy classification systems , 1997 .
[57] Piotr Duda,et al. Adaptation of Decision Trees for Handling Concept Drift , 2013, ICAISC.
[58] I-Cheng Yeh,et al. Modeling slump flow of concrete using second-order regressions and artificial neural networks , 2007 .
[59] L. Rutkowski,et al. Nonparametric recovery of multivariate functions with applications to system identification , 1985, Proceedings of the IEEE.
[60] Piotr Duda,et al. On Pre-processing Algorithms for Data Stream , 2012, ICAISC.
[61] L. Rutkowski,et al. Flexible Takagi-Sugeno fuzzy systems , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..
[62] Hannu Koivisto,et al. A Dynamically Constrained Multiobjective Genetic Fuzzy System for Regression Problems , 2010, IEEE Transactions on Fuzzy Systems.
[63] Piotr Dziwiñski,et al. A New Approach to Nonlinear Modeling Based on Significant Operating Points Detection , 2015, ICAISC.
[64] Thomas Fevens,et al. Web–Based Framework For Breast Cancer Classification , 2014, J. Artif. Intell. Soft Comput. Res..
[65] Yoichi Hayashi,et al. New Method for Dynamic Signature Verification Based on Global Features , 2014, ICAISC.
[66] Leszek Rutkowski,et al. A new algorithm for identity verification based on the analysis of a handwritten dynamic signature , 2016, Appl. Soft Comput..
[67] Marcin Korytkowski,et al. Forecasting Wear of Head and Acetabulum in Hip Joint Implant , 2012, ICAISC.
[68] Leszek Rutkowski,et al. Flexible neuro-fuzzy systems , 2003, IEEE Trans. Neural Networks.
[69] Leszek Rutkowski,et al. Identification of MISO nonlinear regressions in the presence of a wide class of disturbances , 1991, IEEE Trans. Inf. Theory.
[70] Andrew Rosenberg,et al. Multi-objective Genetic Programming for Visual Analytics , 2011, EuroGP.
[71] Robert Nowicki,et al. On design of flexible neuro-fuzzy systems for nonlinear modelling , 2013, Int. J. Gen. Syst..
[72] Marcin Zalasinski,et al. New Approach for the On-Line Signature Verification Based on Method of Horizontal Partitioning , 2013, ICAISC.
[73] Marcin Gabryel,et al. Object Detection by Simple Fuzzy Classifiers Generated by Boosting , 2013, ICAISC.
[74] K. Cpałka. On evolutionary designing and learning of flexible neuro-fuzzy structures for nonlinear classification , 2009 .
[75] Piotr Duda,et al. A New Fuzzy Classifier for Data Streams , 2012, ICAISC.
[76] Brigitte Charnomordic,et al. Generating an interpretable family of fuzzy partitions from data , 2004, IEEE Transactions on Fuzzy Systems.
[77] Feng Liu,et al. A Novel Generic Hebbian Ordering-Based Fuzzy Rule Base Reduction Approach to Mamdani Neuro-Fuzzy System , 2007, Neural Computation.
[78] Aleksandra Rutkowska. Influence Of Membership Function’s Shape On Portfolio Optimization Results , 2016, J. Artif. Intell. Soft Comput. Res..
[79] Francisco Herrera,et al. Integration of an Index to Preserve the Semantic Interpretability in the Multiobjective Evolutionary Rule Selection and Tuning of Linguistic Fuzzy Systems , 2010, IEEE Transactions on Fuzzy Systems.
[80] Jacek Szczypta,et al. Some Aspects of Evolutionary Designing Optimal Controllers , 2013, ICAISC.
[81] Mahdi Eftekhari,et al. Comparison of different T-norm operators in classification problems , 2012, ArXiv.
[82] Jabbar Abbas. The Bipolar Choquet Integrals Based On Ternary-Element Sets , 2016, J. Artif. Intell. Soft Comput. Res..
[83] Piotr Duda,et al. On Resources Optimization in Fuzzy Clustering of Data Streams , 2012, ICAISC.
[84] Tomohiro Yoshikawa,et al. Improvement Of Performance Of Japanese P300 Speller By Using Second Display , 2015, J. Artif. Intell. Soft Comput. Res..
[85] Piotr Duda,et al. Decision Trees for Mining Data Streams Based on the Gaussian Approximation , 2014, IEEE Transactions on Knowledge and Data Engineering.
[86] Leszek Rutkowski,et al. Flexible Takagi Sugeno Neuro Fuzzy Structures for Nonlinear Approximation , 2005 .
[87] José M. Alonso,et al. HILK++: an interpretability-guided fuzzy modeling methodology for learning readable and comprehensible fuzzy rule-based classifiers , 2011, Soft Comput..
[88] Robert Cierniak,et al. On image compression by competitive neural networks and optimal linear predictors , 2000, Signal Process. Image Commun..
[89] Meng Joo Er,et al. On the Weak Convergence of the Orthogonal Series-Type Kernel Regresion Neural Networks in a Non-stationary Environment , 2011, PPAM.
[90] Piotr Duda,et al. Decision Trees for Mining Data Streams Based on the McDiarmid's Bound , 2013, IEEE Transactions on Knowledge and Data Engineering.
[91] Leszek Rutkowski,et al. Neuro-fuzzy systems derived from quasi-triangular norms , 2004, 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542).