A New Approach for Using the Fuzzy Decision Trees for the Detection of the Significant Operating Points in the Nonlinear Modeling

The paper presents a new approach for using the fuzzy decision tress for the detection of the significant operating points from non-invasive measurements of the nonlinear dynamic object. The PSO-GA algorithm is used to identify the unknown values of the system matrix describing the nonlinear dynamic object. It is defined in the terminal nodes of the fuzzy decision tree. The new approach was tested on the nonlinear electrical circuit. The obtained results prove efficiency of the new approach for using fuzzy decision tree for the detection of the significant operating points in the nonlinear modeling.

[1]  Piotr Duda,et al.  Decision Trees for Mining Data Streams Based on the Gaussian Approximation , 2014, IEEE Transactions on Knowledge and Data Engineering.

[2]  Lukasz Bartczuk Gene Expression Programming in Correction Modelling of Nonlinear Dynamic Objects , 2015, ISAT.

[3]  Miltiadis Chalikias,et al.  Maximising Accuracy and Efficiency of Traffic Accident Prediction Combining Information Mining with Computational Intelligence Approaches and Decision Trees , 2014, J. Artif. Intell. Soft Comput. Res..

[4]  Krystian Lapa,et al.  A new approach to design of control systems using genetic programming , 2015, Inf. Technol. Control..

[5]  Piotr Duda,et al.  The CART decision tree for mining data streams , 2014, Inf. Sci..

[6]  Krystian Lapa,et al.  New Algorithm for Evolutionary Selection of the Dynamic Signature Global Features , 2013, ICAISC.

[7]  Meng Joo Er,et al.  Online Speed Profile Generation for Industrial Machine Tool Based on Neuro-fuzzy Approach , 2010, ICAISC.

[8]  Krystian Lapa,et al.  A new method for designing neuro-fuzzy systems for nonlinear modelling with interpretability aspects , 2014, Neurocomputing.

[9]  Chia-Feng Juang,et al.  A hybrid of genetic algorithm and particle swarm optimization for recurrent network design , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[10]  Leszek Rutkowski,et al.  Adaptive probabilistic neural networks for pattern classification in time-varying environment , 2004, IEEE Transactions on Neural Networks.

[11]  K. Cpałka On evolutionary designing and learning of flexible neuro-fuzzy structures for nonlinear classification , 2009 .

[12]  Eren Bas,et al.  The Training Of Multiplicative Neuron Model Based Artificial Neural Networks With Differential Evolution Algorithm For Forecasting , 2016, J. Artif. Intell. Soft Comput. Res..

[13]  Leszek Rutkowski,et al.  New method for the on-line signature verification based on horizontal partitioning , 2014, Pattern Recognit..

[14]  Janusz T. Starczewski,et al.  Interval Type 2 Neuro-Fuzzy Systems Based on Interval Consequents , 2003 .

[15]  Simone A. Ludwig Repulsive Self-Adaptive Acceleration Particle Swarm Optimization Approach , 2014, J. Artif. Intell. Soft Comput. Res..

[16]  Robert Nowicki,et al.  On design of flexible neuro-fuzzy systems for nonlinear modelling , 2013, Int. J. Gen. Syst..

[17]  Piotr Dziwiñski,et al.  A New Algorithm for Identification of Significant Operating Points Using Swarm Intelligence , 2014, ICAISC.

[18]  Petia D. Koprinkova-Hristova,et al.  New Method for Nonlinear Fuzzy Correction Modelling of Dynamic Objects , 2014, ICAISC.

[19]  L. Rutkowski,et al.  Nonparametric recovery of multivariate functions with applications to system identification , 1985, Proceedings of the IEEE.

[20]  Krystian Lapa,et al.  New Method for Design of Fuzzy Systems for Nonlinear Modelling Using Different Criteria of Interpretability , 2014, ICAISC.

[21]  M. Eftekhari,et al.  Extracting interpretable fuzzy models for nonlinear systems using gradient-based continuous ant colony optimization , 2013 .

[22]  Lukasz Bartczuk,et al.  Type-2 Fuzzy Decision Trees , 2006, ICAISC.

[23]  Mehdi Hosseinzadeh Aghdam,et al.  Feature Selection Using Particle Swarm Optimization in Text Categorization , 2015, J. Artif. Intell. Soft Comput. Res..

[24]  Marcin Korytkowski,et al.  From Ensemble of Fuzzy Classifiers to Single Fuzzy Rule Base Classifier , 2006, ICAISC.

[25]  Janusz T. Starczewski,et al.  Learning Methods for Type-2 FLS Based on FCM , 2010, ICAISC.

[26]  Katebi S.A.D.,et al.  Gradient-based Ant Colony Optimization for Continuous Spaces , 2006 .

[27]  L. Rutkowski,et al.  Flexible Takagi-Sugeno fuzzy systems , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[28]  Piotr Dziwiñski,et al.  A New Approach to Nonlinear Modeling Based on Significant Operating Points Detection , 2015, ICAISC.

[29]  Stephen L. Chiu,et al.  Fuzzy Model Identification Based on Cluster Estimation , 1994, J. Intell. Fuzzy Syst..

[30]  Ahmad Fouad El-Samak,et al.  Optimization of Traveling Salesman Problem Using Affinity Propagation Clustering and Genetic Algorithm , 2015, J. Artif. Intell. Soft Comput. Res..

[31]  Muhammad Adil Ansari,et al.  Nonlinear System Identification Using Neural Network , 2012 .

[32]  Piotr Dziwiñski,et al.  Algorithm for Generating Fuzzy Rules for WWW Document Classification , 2006, ICAISC.

[33]  Yoichi Hayashi,et al.  New Fast Algorithm for the Dynamic Signature Verification Using Global Features Values , 2015, ICAISC.

[34]  Yoichi Hayashi,et al.  New Method for Dynamic Signature Verification Based on Global Features , 2014, ICAISC.

[35]  Leszek Rutkowski,et al.  A new algorithm for identity verification based on the analysis of a handwritten dynamic signature , 2016, Appl. Soft Comput..

[36]  Janusz T. Starczewski,et al.  Connectionist Structures of Type 2 Fuzzy Inference Systems , 2001, PPAM.

[37]  Krystian Lapa,et al.  A New Method for Designing and Complexity Reduction of Neuro-fuzzy Systems for Nonlinear Modelling , 2013, ICAISC.

[38]  João Miguel da Costa Sousa,et al.  Decision tree search methods in fuzzy modeling and classification , 2007, Int. J. Approx. Reason..

[39]  Piotr Duda,et al.  Decision Trees for Mining Data Streams Based on the McDiarmid's Bound , 2013, IEEE Transactions on Knowledge and Data Engineering.

[40]  Simone A. Ludwig,et al.  Particle Swarm Optimization Based Fuzzy Clustering Approach to Identify Optimal Number of Clusters , 2014, J. Artif. Intell. Soft Comput. Res..

[41]  Leszek Rutkowski,et al.  Flexible Takagi Sugeno Neuro Fuzzy Structures for Nonlinear Approximation , 2005 .

[42]  Marcin Zalasinski,et al.  Novel Algorithm for the On-Line Signature Verification Using Selected Discretization Points Groups , 2013, ICAISC.

[43]  Krystian Lapa,et al.  A New Approach to Designing Interpretable Models of Dynamic Systems , 2013, ICAISC.

[44]  Marcin Zalasinski,et al.  Novel Algorithm for the On-Line Signature Verification , 2012, ICAISC.

[45]  Marcin Zalasinski,et al.  On-line signature verification using vertical signature partitioning , 2014, Expert Syst. Appl..

[46]  Danuta Rutkowska,et al.  Medical Diagnosis with Type-2 Fuzzy Decision Trees , 2009 .

[47]  Meng Joo Er,et al.  New Method for Dynamic Signature Verification Using Hybrid Partitioning , 2014, ICAISC.

[48]  Leszek Rutkowski,et al.  A New Method for Designing and Reduction of Neuro-Fuzzy Systems , 2006, 2006 IEEE International Conference on Fuzzy Systems.

[49]  Meng Joo Er,et al.  A New Method for the Dynamic Signature Verification Based on the Stable Partitions of the Signature , 2015, ICAISC.

[50]  Jiang Meng,et al.  A Method Research on Nonlinear System Identification Based on Neural Network , 2012 .

[51]  Krzysztof Cpalka,et al.  A New Method to Construct of Interpretable Models of Dynamic Systems , 2012, ICAISC.

[52]  Krzysztof Cpalka A Method for Designing Flexible Neuro-fuzzy Systems , 2006, ICAISC.

[53]  A. N. Jha,et al.  Nonlinear System Identification using Neural Networks , 2007 .

[54]  Piotr Dziwiñski,et al.  A New Method of the Intelligent Modeling of the Nonlinear Dynamic Objects with Fuzzy Detection of the Operating Points , 2016, ICAISC.