최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기Neurocomputing, v.262, 2017년, pp.4 - 27
Pratama, M. , Lughofer, E. , Er, M.J. , Anavatti, S. , Lim, C.P.
The Metacognitive Scaffolding Learning Machine (McSLM), combining the concept of metacognition-what-to-learn, how-to-learn, and when-to-learn, and the Scaffolding theory-a tutoring theory for a learner to learn a complex task, has been successfully developed to enhance the capability of Evolving Int...
IEEE Trans. Indust. Electron. Abiyev 55 8 3133 2008 10.1109/TIE.2008.924018 Fuzzy wavelet neural networks for identification and control of dynamic plants-a novel structure and a comparative study
IEEE Trans. Indust. Electron. Abiyev 57 12 4147 2010 10.1109/TIE.2010.2043036 Type-2 fuzzy neural structure for identification and control of time-varying plants
J. Franklin Inst. B Abiyev 550 1658 2012 A type-2 fuzzy wavelet neural network for system identification and control
IEEE Trans. Syst. Man Cybern. Part B: Cybern. Angelov 34 484 2004 10.1109/TSMCB.2003.817053 An approach to online identification of Takagi-Sugeno fuzzy models
Angelov 1068 2005 IEEE International Conference on Fuzzy Systems (FUZZ) Simpl_eTS: a simplified method for learning evolving Takagi-Sugeno fuzzy models
Angelov 21 2010 Evolving Intelligent Systems: Methodology and Applications Evolving Takagi-Sugeno fuzzy systems from data streams (eTS+)
IEEE Trans. Syst. Man Cybern. Part B: Cybern. Angelov 41 4 898 2011 10.1109/TSMCB.2010.2098866 Fuzzily connected multi-model systems evolving autonomously from data streams
Int. J. Gen. Syst. Angelov 41 2 163 2012 10.1080/03081079.2011.634807 A new type of simplified fuzzy rule-based system
IEEE Trans. Fuzzy Syst. Bustince 2015 Interval type-2 fuzzy sets are generalization of interval-valued fuzzy sets: towards a wider view on their relationship
IEEE Trans. Fuzzy Syst. Bouchachia 22 4 999 2014 10.1109/TFUZZ.2013.2279554 GT2FC: an online growing interval type-2 self-learning fuzzy classifier
IEEE Trans. Neural Networks Bose 20 6 1039 2009 10.1109/TNN.2009.2019270 A growing and pruning method for radial basis function networks
IEEE Trans. Neural Networks Learning Syst. van der Aalst 25 1 154 2014 10.1109/TNNLS.2013.2278313 Dealing with concept drifts in process mining
IEEE Trans. Fuzzy Syst. Das 23 6 2080 2015 10.1109/TFUZZ.2015.2403793 An evolving interval type-2 neurofuzzy inference system and its metacognitive sequential learning algorithm
IEEE Trans. Knowl. Data Eng. Ditzler 25 10 2283 2012 10.1109/TKDE.2012.136 Incremental learning of concept drift from streaming imbalanced data
IEEE Trans. Neural Netw. Elwell 22 10 1517 2011 10.1109/TNN.2011.2160459 Incremental learning of concept drift in non-stationary environments
Psychol. Sci. Flavell 7 4 200 1996 10.1111/j.1467-9280.1996.tb00359.x Piagiet's legacy
Inf. Sci. Ganjefar 294 269 2014 10.1016/j.ins.2014.09.054 Single-hidden-layer fuzzy recurrent wavelet neural network: applications to function approximation and system identification
IEEE Trans. Cybern. Gan 44 4 554 2014 10.1109/TCYB.2013.2260537 Nonlinear systems modeling based on self-organizing fuzzy-neural-network with adaptive computation algorithm
Inf. Sci. Hajmohammadi 317 67 2015 10.1016/j.ins.2015.04.003 Combination of active learning and self-training for cross-lingual sentiment classification with density analysis of unlabelled samples
IEEE Trans. Syst. Man Cybern. Part-B: Cybern. Huang 34 2284 2004 10.1109/TSMCB.2004.834428 An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks
IEEE Trans. Neural Networks Huang 16 57 2005 10.1109/TNN.2004.836241 A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation
J. Scholarship Teaching Learn. Isaacson 6 1 39 2006 Metacognitive knowledge monitoring and self-regulated learning: academic success and reflection on learning
IEEE Trans. Syst. Man Cybern. Part b: Cybern. Jang 23 665 1993 10.1109/21.256541 ANFIS: adaptive-network-based fuzzy inference system
IEEE Trans. Neural Netw. Juang 10 828 1999 10.1109/72.774232 A recurrent self-organizing neural fuzzy inference network
IEEE Trans. Fuzzy Syst. Juang 16 6 1411 2008 10.1109/TFUZZ.2008.925907 A self-evolving interval type-2 fuzzy neural network with online structure and parameter learning
Fuzzy Sets Syst. Juang 161 19 2552 2010 10.1016/j.fss.2010.04.006 A recurrent self-evolving fuzzy neural network with local feedbacks and its application to dynamic system processing
IEEE Trans. Cybern. Juang 43 6 1781 2013 10.1109/TSMCB.2012.2230253 Data-driven interval type-2 neural fuzzy system with high learning accuracy and improved model interpretability
Joysula 1419 2009 Proceeding of World Congress on Nature and Biologically Inspired Computing Modeling metacognition for learning in artificial systems
Inf. Sci. Lemos 220 64 2013 10.1016/j.ins.2011.08.030 Adaptive fault detection and diagnosis using an evolving fuzzy classifier
Lewis 148 1994 Proceedings of the International Conference on Machine Learning Heterogeneous uncertainty sampling for supervised learning
IEEE Trans. Neural Networks Learn. Syst. Lin 24 2 310 2012 10.1109/TNNLS.2012.2231436 Identification and prediction of dynamic systems using an interactively recurrent self-evolving fuzzy neural network
IEEE Trans. Fuzzy Syst. Lin 21 3 492 2013 10.1109/TFUZZ.2013.2255613 A mutually recurrent interval type-2 neural fuzzy system (MRIT2NFS) with self-evolving structure and parameters
IEEE Trans. Ind. Electron. Lin 61 1 447 2014 10.1109/TIE.2013.2248332 A TSK-type-based self-evolving compensatory interval type-2 fuzzy neural network (TSCIT2FNN) and its applications
IEEE Trans. Neural Networks Learn. Syst. Lin 25 5 959 2014 10.1109/TNNLS.2013.2284603 Simplified interval type-2 fuzzy neural networks
IEEE Trans. Fuzzy Syst. Liang 8 5 535 2000 10.1109/91.873577 Interval type-2 fuzzy logic systems: theory and design
Lima 67 2010 Evolving intelligent systems: methodology and applications Evolving fuzzy modelling using participatory learning
IEEE Trans. Fuzzy Syst. Lughofer 16 6 1393 2008 10.1109/TFUZZ.2008.925908 FLEXFIS: a robust incremental learning approach for evolving Takagi-Sugeno fuzzy models
Fuzzy Sets Syst. Lughofer 163 1 1 2011 10.1016/j.fss.2010.08.012 On-line incremental feature weighting in evolving fuzzy classifiers
Lughofer 2011 Evolving Fuzzy Systems-Methodologies, Advanced Concepts and Applications
Appl. Soft Comput. Lughofer 11 2487 2011 10.1016/j.asoc.2010.10.004 Identifying static and dynamic prediction models for Nox emissions with evolving fuzzy systems
Inf. Sci. Lughofer 181 5123 2011 10.1016/j.ins.2011.07.012 On the employing fuzzy modeling algorithms for the valuation of the residential premises
Evolv. Syst. Lughofer 2 3 165 2011 10.1007/s12530-011-9032-3 On-line elimination of local redundancies in evolving fuzzy systems
Lughofer 205 2012 Learning in Non-Stationary Environments: Methods and Applications Flexible evolving fuzzy inference systems from data streams (FLEXFIS++)
Inf. Sci. Lughofer 251 22 2013 10.1016/j.ins.2013.07.002 On-line assurance of interpretability criteria in evolving fuzzy systems-achievements, new concepts and open issues
Inf. Sci. Lughofer 204 54 2015 10.1016/j.ins.2015.01.010 Autonomous data stream clustering implementing incremental split-and-merge techniques-towards a plug-and-play approach
Evolv. Syst. Lughofer 6 4 54 2015 10.1007/s12530-015-9132-6 Generalized smart evolving fuzzy systems
IEEE Trans. Pattern Anal. Mach. Intell. Mitra 24 3 301 2002 10.1109/34.990133 Unsupervised feature selection using feature similarity
Commun. Nonlinear Sci. Numer. Simul. Mazandarani 19 710 2014 10.1016/j.cnsns.2013.07.002 Differentiability of type-2 fuzzy number-valued functions
Commun. Nonlinear Sci. Numer. Simul. Mazandarani 19 2354 2014 10.1016/j.cnsns.2013.11.003 Type-2 fuzzy fractional derivatives
Expert Syst. Appl. Oentaryo 41 11 5082 2014 10.1016/j.eswa.2014.01.034 Online probabilistic learning for fuzzy inference systems
IEEE Trans. Syst. Man Cybern.-Part B: Cybern. Patra 32 4 505 2002 10.1109/TSMCB.2002.1018769 Nonlinear dynamic system identification using Chebyshev functional link artificial neural networks
IEEE Trans. Neural Networks Learn. Syst. Pratama 25 1 55 2014 10.1109/TNNLS.2013.2271933 PANFIS: a novel incremental learning machine
IEEE Trans. Fuzzy Syst. Pratama 22 3 547 2014 10.1109/TFUZZ.2013.2264938 GENEFIS: towards an effective localist network
Pratama 369 2014 Proceeding of 2014 International Conference on Fuzzy Systems A novel meta-cognitive-based scaffolding classifier to sequential non-stationary classification problems
IEEE Trans. Fuzzy Syst. Pratama 23 6 2048 2015 10.1109/TFUZZ.2015.2402683 Recurrent classifier based on an incremental meta-cognitive scaffolding algorithm
Neurocomputing Pratama 171 89 2015 10.1016/j.neucom.2015.06.022 An incremental meta-cognitive-based scaffolding fuzzy neural network
IEEE Trans. Fuzzy Syst. Pratama 2015 Evolving type-2 fuzzy classifier
Neurocomputing Pratama 2016 10.1016/j.neucom.2016.01.049 Scaffolding type-2 classifier for incremental learning under concept drifts
Fuzzy Sets Syst. Rong 157 9 1260 2006 10.1016/j.fss.2005.12.011 Sequential adaptive fuzzy inference system (SAFIS) for nonlinear system identification and time series prediction
Evolv. Syst. Rong 2 2 71 2011 10.1007/s12530-010-9023-9 Extended sequential adaptive fuzzy inference system for classification problems
Neural Comput. Savitha 24 5 1297 2012 10.1162/NECO_a_00254 Metacognitive learning in a fully complex-valued radial basis function neural network
Settles 2010 Active Learning Literature Survey
IEEE Trans. Fuzzy Syst. Subramanian 21 6 1080 2014 10.1109/TFUZZ.2013.2242894 A meta-cognitive neuro-fuzzy inference system (McFIS) for sequential classification systems
Evolv. Syst. Subramanian 5 4 219 2014 10.1007/s12530-013-9102-9 A meta-cognitive interval type-2 fuzzy inference system and its projection based learning algorithm
Neurocomputing Suresh 73 16 3012 2010 10.1016/j.neucom.2010.07.003 A sequential learning algorithm for self-adaptive resource allocation network classifier,
J. R. Stat. Soc. Stone 36 111 1974 10.1111/j.2517-6161.1974.tb00994.x Cross-validatory choice and assessment of statistical predictions
Pattern Recognit. Tabata 43 9 3162 2010 10.1016/j.patcog.2010.03.012 Data compression by volume prototypes for streaming data
Inf. Sci. Tung 220 124 2013 10.1016/j.ins.2012.02.031 eT2FIS: an evolving type-2 neural fuzzy inference system
Neural Netw. Vukovic 46 210 2013 10.1016/j.neunet.2013.06.004 A growing and pruning sequential learning algorithm of hyper basis function neural network for function approximation
Vygotsky 1978 Mind and Society: The Development of Higher Psychological Processes
IEEE Trans. Neural Netw. Vigdor 18 6 1628 2007 10.1109/TNN.2007.900234 The Bayesian ARTMAP
Neurocomputing Wang 72 3818 2009 10.1016/j.neucom.2009.05.006 Fast and accurate self organizing scheme for parsimonious fuzzy neural Network
Int. J. Artif. Intell. Educ. Wood 12 3 280 2001 Scaffolding contingent tutoring and computer-based learning
IEEE Trans. Syst. Man Cybern. Part b: Cybern. Wu 30 358 2000 10.1109/3477.836384 Dynamic fuzzy neural networks-a novel approach to function approximation
IEEE Trans. Fuzzy Syst. Wu 9 4 578 2003 A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks
IEEE Trans. Neural Netw. Xu 17 1 19 2006 10.1109/TNN.2005.860857 Generalized recursive least square to the training of neural network
IEEE Trans. Knowl. Data Eng. Xiong 26 1 43 2014 10.1109/TKDE.2013.22 Active learning of constraints for semi-supervised clustering
IEEE. Trans. Syst. Man. Cybern. Yager 24 8 1279 1994 10.1109/21.299710 Approximate clustering via the mountain method
J. Mach. Learn. Res. Yu 5 1205 2004 Efficient feature selection via analysis of relevance and redundancy
Inf. Sci. Zadeh 8 3 199 1975 10.1016/0020-0255(75)90036-5 The concept of a linguistic variable and its application to approximate reasoning
IEEE Trans. Neural Networks Learn. Syst. Zliobaite 25 1 27 2014 10.1109/TNNLS.2012.2236570 Active learning with drifting streaming data
IEEE Trans. Fuzzy Syst. Das 23 6 2080 2015 10.1109/TFUZZ.2015.2403793 An Evolving Interval Type-2 Neurofuzzy Inference System and Its Metacognitive Sequential Learning Algorithm
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.