New Method for Non-linear Correction Modelling of Dynamic Objects with Genetic Programming

In the paper a method to adapt the equivalent linearization technique of the non-linear state equation is proposed. This algorithm uses correction matrices. It also uses arrays amendments which elements are determined for each new point. These elements are generated by a formula created automatically using genetic programming.

[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.