New Method for Non-linear Correction Modelling of Dynamic Objects with Genetic Programming
暂无分享,去创建一个
[1] Marcin Zalasinski,et al. Novel Algorithm for the On-Line Signature Verification , 2012, ICAISC.
[2] Alexander I. Galushkin,et al. The Parallel Approach to the Conjugate Gradient Learning Algorithm for the Feedforward Neural Networks , 2014, ICAISC.
[3] Piotr Dziwiñski,et al. Hybrid State Variables - Fuzzy Logic Modelling of Nonlinear Objects , 2013, ICAISC.
[4] Marcin Zalasinski,et al. On-line signature verification using vertical signature partitioning , 2014, Expert Syst. Appl..
[5] Meng Joo Er,et al. New Method for Dynamic Signature Verification Using Hybrid Partitioning , 2014, ICAISC.
[6] Jaroslaw Bilski. Momentum Modification of the RLS Algorithms , 2004, ICAISC.
[7] Leszek Rutkowski,et al. A New Method for Designing and Reduction of Neuro-Fuzzy Systems , 2006, 2006 IEEE International Conference on Fuzzy Systems.
[8] L. Rutkowski,et al. Flexible Takagi-Sugeno fuzzy systems , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..
[9] Jaroslaw Bilski,et al. Parallel Approach to Learning of the Recurrent Jordan Neural Network , 2013, ICAISC.
[10] Janusz T. Starczewski,et al. New Linguistic Hedges in Construction of Interval Type-2 FLS , 2010, ICAISC.
[11] A. Jordan. Linearization of non-linear state equation , 2006 .
[12] Piotr Duda,et al. Decision Trees for Mining Data Streams Based on the Gaussian Approximation , 2014, IEEE Transactions on Knowledge and Data Engineering.
[13] Marcin Zalasinski,et al. Novel Algorithm for the On-Line Signature Verification Using Selected Discretization Points Groups , 2013, ICAISC.
[14] Krystian Lapa,et al. A New Approach to Designing Interpretable Models of Dynamic Systems , 2013, ICAISC.
[15] B. de Fornel,et al. Commande optimale d'un système générateur photovoltaïque-convertisseur statique - récepteur , 1984 .
[16] Leszek Rutkowski,et al. New method for the on-line signature verification based on horizontal partitioning , 2014, Pattern Recognit..
[17] Cândida Ferreira. Gene Expression Programming in Problem Solving , 2002 .
[18] Krzysztof Cpalka,et al. A New Method to Construct of Interpretable Models of Dynamic Systems , 2012, ICAISC.
[19] Mietek A. Brdys,et al. Optimizing Control by Robustly Feasible Model Predictive Control and Application to Drinking Water Distribution Systems , 2009, ICANN.
[20] Jaroslaw Bilski,et al. Parallel Realisation of QR Algorithm for Neural Networks Learning , 2004, ICAISC.
[21] Dimitris C. Theodoridis,et al. Robustifying analysis of the direct adaptive control of unknown multivariable nonlinear systems based on a new neuro-fuzzy method , 2011 .
[22] Janusz T. Starczewski,et al. A New Method for Dealing with Unbalanced Linguistic Term Set , 2012, ICAISC.
[23] T. Caughey. Equivalent Linearization Techniques , 1962 .
[24] K. Cpałka. On evolutionary designing and learning of flexible neuro-fuzzy structures for nonlinear classification , 2009 .
[25] Leszek Rutkowski. On Bayes Risk Consistent Pattern Recognition Procedures in a Quasi-Stationary Environment , 1982, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[26] Ali Chaibakhsh,et al. Orthonormal basis function fuzzy systems for biological wastewater treatment processes modeling , 2012, SOCO 2012.
[27] Petia D. Koprinkova-Hristova,et al. New Method for Nonlinear Fuzzy Correction Modelling of Dynamic Objects , 2014, ICAISC.
[28] Piotr Duda,et al. The CART decision tree for mining data streams , 2014, Inf. Sci..
[29] Krystian Lapa,et al. New Algorithm for Evolutionary Selection of the Dynamic Signature Global Features , 2013, ICAISC.
[30] Meng Joo Er,et al. Online Speed Profile Generation for Industrial Machine Tool Based on Neuro-fuzzy Approach , 2010, ICAISC.
[31] Janusz T. Starczewski,et al. Fully Controllable Ant Colony System for Text Data Clustering , 2012, ICAISC.
[32] Cândida Ferreira,et al. Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence , 2014, Studies in Computational Intelligence.
[33] Narasimhan Sundararajan,et al. Neural-Sliding Mode Augmented Robust Controller for Autolanding of Fixed Wing Aircraft , 2013, SOCO 2013.
[34] Komla A. Folly. Parallel Pbil Applied to Power System Controller Design , 2013, J. Artif. Intell. Soft Comput. Res..
[35] Romis de Faissol Attux,et al. Magnetic particle swarm optimization , 2011, 2011 IEEE Symposium on Swarm Intelligence.
[36] Valder Steffen,et al. Solution of singular optimal control problems using the improved differential evolution algorithm , 2011 .
[37] Leszek Rutkowski,et al. Neuro-fuzzy systems derived from quasi-triangular norms , 2004, 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542).
[38] Beatriz Pérez-Sánchez,et al. Learning from heterogeneously distributed data sets using artificial neural networks and genetic algorithms , 2012, SOCO 2012.
[39] Janusz T. Starczewski,et al. New Method for Generation Type-2 Fuzzy Partition for FDT , 2010, ICAISC.
[40] Piotr Dziwiñski,et al. A New Algorithm for Identification of Significant Operating Points Using Swarm Intelligence , 2014, ICAISC.
[41] L. Rutkowski. On-line identification of time-varying systems by nonparametric techniques , 1982 .
[42] Jaroslaw Bilski,et al. Parallel Architectures for Learning the RTRN and Elman Dynamic Neural Networks , 2015, IEEE Transactions on Parallel and Distributed Systems.
[43] L. Rutkowski,et al. A neuro-fuzzy controller with a compromise fuzzy reasoning , 2002 .
[44] Cândida Ferreira,et al. Gene Expression Programming: A New Adaptive Algorithm for Solving Problems , 2001, Complex Syst..
[45] PETIA KOPRINKOVA-HRISTOVA,et al. Backpropagation through Time Training of a Neuro-Fuzzy Controller , 2010, Int. J. Neural Syst..
[46] Jaroslaw Bilski,et al. Parallel Realisation of the Recurrent Multi Layer Perceptron Learning , 2012, ICAISC.
[47] L. Rutkowski. On nonparametric identification with prediction of time-varying systems , 1984 .
[48] Leszek Rutkowski,et al. Numerically Robust Learning Algorithms for Feed Forward Neural Networks , 2003 .
[49] Krzysztof Patan,et al. Optimal training strategies for locally recurrent neural networks , 2011 .
[50] Krystian Lapa,et al. A new method for designing neuro-fuzzy systems for nonlinear modelling with interpretability aspects , 2014, Neurocomputing.
[51] Jaroslaw Bilski,et al. Parallel Realisation of the Recurrent RTRN Neural Network Learning , 2008, ICAISC.
[52] Marcin Zalasinski,et al. New Approach for the On-Line Signature Verification Based on Method of Horizontal Partitioning , 2013, ICAISC.
[53] Candida Ferreira. Gene expression programming , 2006 .
[54] Yoichi Hayashi,et al. New Method for Dynamic Signature Verification Based on Global Features , 2014, ICAISC.
[55] Leszek Rutkowski. Multiple Fourier series procedures for extraction of nonlinear regressions from noisy data , 1993, IEEE Trans. Signal Process..
[56] Krystian Lapa,et al. A New Method for Designing and Complexity Reduction of Neuro-fuzzy Systems for Nonlinear Modelling , 2013, ICAISC.
[57] Leszek Rutkowski,et al. Novel Online Speed Profile Generation for Industrial Machine Tool Based on Flexible Neuro-Fuzzy Approximation , 2012, IEEE Transactions on Industrial Electronics.