Nonlinear system identification based on a self-organizing type-2 fuzzy RBFN

This paper presents a new self-evolving recurrent Type-2 Fuzzy Radial Basis Function Network (T2FRBFN) in which the weights are considered Gaussian type-2 fuzzy sets and uncertain mean in each RBF neuron. The capability of the proposed T2FRBFN for function approximation and dynamical system identification perform better than the conventional RBFN. A novel type-2 fuzzy clustering is presented to add or remove the hidden RBF neurons. For parameter learning, back-propagation with adaptive learning rate is used. Finally the proposed T2FRBFN is applied to identification of three nonlinear systems as case studies. A comparison between T2FRBFN and the conventional RBFN as well as the method ofźRubio-Solis and Panoutsos (2015) is presented. Simulation results and their statistical description show that the proposed T2FRBFN perform better than the conventional RBFN.

[1]  Ali Doustmohammadi,et al.  An adaptive wavelet differential neural networks based identifier and its stability analysis , 2012, Neurocomputing.

[2]  Feng Dai,et al.  Pointwise approximation with quasi-interpolation by radial basis functions , 2015, J. Approx. Theory.

[3]  Junfei Qiao,et al.  An online self-adaptive modular neural network for time-varying systems , 2014, Neurocomputing.

[4]  Wensheng Zhang,et al.  Generalization Performance of Radial Basis Function Networks , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[5]  Ali Selamat,et al.  Modeling permeability and PVT properties of oil and gas reservoir using hybrid model based on type-2 fuzzy logic systems , 2015, Neurocomputing.

[6]  Hao Yu,et al.  An Incremental Design of Radial Basis Function Networks , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[7]  Jerry M. Mendel,et al.  Type-2 fuzzy logic systems , 1999, IEEE Trans. Fuzzy Syst..

[8]  Nasir A. Al-geelani,et al.  Sugeno fuzzy PID tuning, by genetic-neutral for AVR in electrical power generation , 2015, Appl. Soft Comput..

[9]  M. Sabahi,et al.  Self-adaptive RBF neural network PID controller in linear elevator , 2011, 2011 International Conference on Electrical Machines and Systems.

[10]  Amir Abolfazl Suratgar,et al.  A New Method For Linguistic Modeling With Stability Analysis And Applications , 2009, Intell. Autom. Soft Comput..

[11]  Hak-Keung Lam,et al.  Interval type-2 fuzzy control for nonlinear discrete-time systems with time-varying delays , 2015, Neurocomputing.

[12]  Byung-In Choi,et al.  Interval Type-2 Fuzzy Membership Function Design and its Application to Radial Basis Function Neural Networks , 2007, 2007 IEEE International Fuzzy Systems Conference.

[13]  Udo Seiffert,et al.  Fusion trees for fast and accurate classification of hyperspectral data with ensembles of $$\gamma$$γ-divergence-based RBF networks , 2014, Neural Computing and Applications.

[14]  James M. Keller,et al.  A possibilistic approach to clustering , 1993, IEEE Trans. Fuzzy Syst..

[15]  Mohammad Bagher Menhaj,et al.  Stability analysis of recurrent type-2 TSK fuzzy systems with nonlinear consequent part , 2015, Neural Computing and Applications.

[16]  Mohammad Bagher Menhaj,et al.  Stable ANFIS2 for Nonlinear System Identification , 2016, Neurocomputing.

[17]  Fayez F. M. El-Sousy.  Adaptive hybrid control system using a recurrent RBFN-based self-evolving fuzzy-neural-network for PMSM servo drives , 2014, Appl. Soft Comput..

[18]  Chao Cai,et al.  Route evaluation for unmanned aerial vehicle based on type-2 fuzzy sets , 2015, Eng. Appl. Artif. Intell..

[19]  C. P. Wang,et al.  An intelligent sales forecasting system through integration of artificial neural networks and fuzzy neural networks with fuzzy weight elimination , 2002, Neural Networks.

[20]  Mark S. Varvak,et al.  Ellipsoidal/radial basis functions neural networks enhanced with the Rvachev function method in application problems , 2015, Eng. Appl. Artif. Intell..

[21]  Ruey-Jing Lian,et al.  Adaptive Self-Organizing Fuzzy Sliding-Mode Radial Basis-Function Neural-Network Controller for Robotic Systems , 2014, IEEE Transactions on Industrial Electronics.

[22]  Türkay Dereli,et al.  Industrial applications of type-2 fuzzy sets and systems: A concise review , 2011, Comput. Ind..

[23]  Jafar Tavoosi,et al.  A class of type-2 fuzzy neural networks for nonlinear dynamical system identification , 2012, Neural Computing and Applications.

[24]  Mohammad Hossein Fazel Zarandi,et al.  A new cluster validity measure based on general type-2 fuzzy sets: Application in gene expression data clustering , 2014, Knowl. Based Syst..

[25]  Tzyy-Chyang Lu,et al.  Genetic-algorithm-based type reduction algorithm for interval type-2 fuzzy logic controllers , 2015, Eng. Appl. Artif. Intell..

[26]  Witold Pedrycz,et al.  Cluster-Centric Fuzzy Modeling , 2014, IEEE Transactions on Fuzzy Systems.

[27]  Asifullah Khan,et al.  Robust fuzzy RBF network based image segmentation and intelligent decision making system for carotid artery ultrasound images , 2015, Neurocomputing.

[28]  George E. Tsekouras,et al.  Improving the effect of fuzzy clustering on RBF network's performance in terms of particle swarm optimization , 2015, Adv. Eng. Softw..

[29]  Junfei Qiao,et al.  Identification and modeling of nonlinear dynamical systems using a novel self-organizing RBF-based approach , 2012, Autom..

[30]  Jeen-Shing Wang,et al.  A Hammerstein Recurrent Neurofuzzy Network With an Online Minimal Realization Learning Algorithm , 2008, IEEE Transactions on Fuzzy Systems.

[31]  Oscar Castillo,et al.  An Interval Type-2 Fuzzy Logic Toolbox for Control Applications , 2007, 2007 IEEE International Fuzzy Systems Conference.

[32]  Witold Pedrycz,et al.  Type-2 Fuzzy Logic: Theory and Applications , 2007, 2007 IEEE International Conference on Granular Computing (GRC 2007).

[33]  George Panoutsos,et al.  Interval Type-2 Radial Basis Function Neural Network: A Modeling Framework , 2015, IEEE Transactions on Fuzzy Systems.

[34]  Quang Phuc Ha,et al.  Enhanced radial basis function neural networks for ozone level estimation , 2015, Neurocomputing.

[35]  Oscar Castillo,et al.  Interval type-2 fuzzy weight adjustment for backpropagation neural networks with application in time series prediction , 2014, Inf. Sci..