A new data-driven neural fuzzy system with collaborative fuzzy clustering mechanism

In this paper, a novel fuzzy rule transfer mechanism for self-constructing neural fuzzy inference networks is being proposed. The features of the proposed method, termed data-driven neural fuzzy system with collaborative fuzzy clustering mechanism (DDNFS-CFCM) are; (1) Fuzzy rules are generated facilely by fuzzy c-means (FCM) and then adapted by the preprocessed collaborative fuzzy clustering (PCFC) technique, and (2) Structure and parameter learning are performed simultaneously without selecting the initial parameters. The DDNFS-CFCM can be applied to deal with big data problems by the virtue of the PCFC technique, which is capable of dealing with immense datasets while preserving the privacy and security of datasets. Initially, the entire dataset is organized into two individual datasets for the PCFC procedure, where each of the dataset is clustered separately. The knowledge of prototype variables (cluster centers) and the matrix of just one halve of the dataset through collaborative technique are deployed. The DDNFS-CFCM is able to achieve consistency in the presence of collective knowledge of the PCFC and boost the system modeling process by parameter learning ability of the self-constructing neural fuzzy inference networks (SONFIN). The proposed method outperforms other existing methods for time series prediction problems.

[1]  Narasimhan Sundararajan,et al.  A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation , 2005, IEEE Transactions on Neural Networks.

[2]  Ruei-Cheng Wu,et al.  Estimating driving performance based on EEG spectrum and fuzzy neural network , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[3]  Chin-Teng Lin,et al.  A recurrent neural fuzzy network for word boundary detection in variable noise-level environments , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[4]  Chia-Feng Juang,et al.  Speedup of Implementing Fuzzy Neural Networks With High-Dimensional Inputs Through Parallel Processing on Graphic Processing Units , 2011, IEEE Transactions on Fuzzy Systems.

[5]  T. Martin McGinnity,et al.  Design for Self-Organizing Fuzzy Neural Networks Based on Genetic Algorithms , 2006, IEEE Transactions on Fuzzy Systems.

[6]  Paramasivan Saratchandran,et al.  Sequential Adaptive Fuzzy Inference System (SAFIS) for nonlinear system identification and prediction , 2006, Fuzzy Sets Syst..

[7]  Plamen P. Angelov,et al.  Simpl_eTS: a simplified method for learning evolving Takagi-Sugeno fuzzy models , 2005, The 14th IEEE International Conference on Fuzzy Systems, 2005. FUZZ '05..

[8]  Meng Joo Er,et al.  A fast and accurate online self-organizing scheme for parsimonious fuzzy neural networks , 2009, Neurocomputing.

[9]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[10]  Kwang Bo Cho,et al.  Radial basis function based adaptive fuzzy systems and their applications to system identification and prediction , 1996, Fuzzy Sets Syst..

[11]  John C. Platt.  A Resource-Allocating Network for Function Interpolation , 1991, Neural Computation.

[12]  J. J. Shann,et al.  A fuzzy neural network for rule acquiring on fuzzy control systems , 1995 .

[13]  Chia-Feng Juang,et al.  Skin color segmentation by histogram-based neural fuzzy network , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[14]  Bu-Sung Lee,et al.  A fuzzy neural network with fuzzy impact grades , 2009, Neurocomputing.

[15]  Meng Joo Er,et al.  A fast learning algorithm for parsimonious fuzzy neural systems , 1999, 1999 European Control Conference (ECC).

[16]  Mohammad Mehdi Ebadzadeh,et al.  A novel hybrid algorithm for creating self-organizing fuzzy neural networks , 2009, Neurocomputing.

[17]  Witold Pedrycz,et al.  Collaborative fuzzy clustering , 2002, Pattern Recognit. Lett..

[18]  Chin-Teng Lin,et al.  An On-Line Self-Constructing Neural Fuzzy Inference Network and Its Applications , 1998 .

[19]  Li-Xin Wang,et al.  Adaptive fuzzy systems and control - design and stability analysis , 1994 .

[20]  Cheng-Hung Chen,et al.  Nonlinear system control using self-evolving neural fuzzy inference networks with reinforcement evolutionary learning , 2011, Appl. Soft Comput..

[21]  Narasimhan Sundararajan,et al.  A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks , 2006, IEEE Transactions on Neural Networks.

[22]  Igor Skrjanc,et al.  Recursive fuzzy c-means clustering for recursive fuzzy identification of time-varying processes. , 2011, ISA transactions.

[23]  T. Martin McGinnity,et al.  An on-line algorithm for creating self-organizing fuzzy neural networks , 2004, Neural Networks.

[24]  Chia-Feng Juang,et al.  A Self-Evolving Interval Type-2 Fuzzy Neural Network With Online Structure and Parameter Learning , 2008, IEEE Transactions on Fuzzy Systems.

[25]  Héctor Pomares,et al.  Time series analysis using normalized PG-RBF network with regression weights , 2002, Neurocomputing.

[26]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[27]  Meng Joo Er,et al.  Dynamic fuzzy neural networks-a novel approach to function approximation , 2000, IEEE Trans. Syst. Man Cybern. Part B.

[28]  Ajith Abraham,et al.  Neuro Fuzzy Systems: Sate-of-the-Art Modeling Techniques , 2001, IWANN.

[29]  Satish Kumar,et al.  Asymmetric subsethood-product fuzzy neural inference system (ASuPFuNIS) , 2005, IEEE Transactions on Neural Networks.

[30]  Chin-Teng Lin,et al.  Vertical Collaborative Fuzzy C-Means for Multiple EEG Data Sets , 2013, ICIRA.

[31]  Chin-Teng Lin,et al.  EEG-Based Learning System for Online Motion Sickness Level Estimation in a Dynamic Vehicle Environment , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[32]  Y Lu,et al.  A Sequential Learning Scheme for Function Approximation Using Minimal Radial Basis Function Neural Networks , 1997, Neural Computation.

[33]  Chin-Teng Lin,et al.  A TSK-Type-Based Self-Evolving Compensatory Interval Type-2 Fuzzy Neural Network (TSCIT2FNN) and Its Applications , 2014, IEEE Transactions on Industrial Electronics.

[34]  Jyh-Yeong Chang,et al.  Designing mamdani type fuzzy rule using a collaborative FCM scheme , 2013, 2013 International Conference on Fuzzy Theory and Its Applications (iFUZZY).

[35]  Chin-Teng Lin,et al.  Takagi-Sugeno-Kang type collaborative fuzzy rule based system , 2014, 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM).

[36]  Mohammad Mehdi Ebadzadeh,et al.  Three new fuzzy neural networks learning algorithms based on clustering, training error and genetic algorithm , 2011, Applied Intelligence.

[37]  C. S. George Lee,et al.  Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems , 1996 .

[38]  Chin-Teng Lin,et al.  A preprocessed induced partition matrix based collaborative fuzzy clustering for data analysis , 2014, 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[39]  Junfei Qiao,et al.  A Self-Organizing Fuzzy Neural Network Based on a Growing-and-Pruning Algorithm , 2010, IEEE Transactions on Fuzzy Systems.

[40]  Chin-Teng Lin,et al.  Collaborative fuzzy rule learning for Mamdani type fuzzy inference system with mapping of cluster centers , 2014, 2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA).

[41]  J. Buckley,et al.  Fuzzy neural networks: a survey , 1994 .

[42]  Jiwen Dong,et al.  Time-series prediction using a local linear wavelet neural network , 2006, Neurocomputing.

[43]  Chin-Teng Lin,et al.  An online self-constructing neural fuzzy inference network and its applications , 1998, IEEE Trans. Fuzzy Syst..

[44]  Shang-Liang Chen,et al.  Orthogonal least squares learning algorithm for radial basis function networks , 1991, IEEE Trans. Neural Networks.

[45]  Jeen-Shing Wang,et al.  Self-adaptive neuro-fuzzy inference systems for classification applications , 2002, IEEE Trans. Fuzzy Syst..

[46]  D.P. Filev,et al.  An approach to online identification of Takagi-Sugeno fuzzy models , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[47]  Witold Pedrycz,et al.  Knowledge-based clustering - from data to information granules , 2007 .

[48]  Ning Wang,et al.  A Generalized Ellipsoidal Basis Function Based Online Self-constructing Fuzzy Neural Network , 2011, Neural Processing Letters.

[49]  W. Klimesch.  EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis , 1999, Brain Research Reviews.

[50]  Wan-Jui Lee,et al.  Constructing neuro-fuzzy systems with TSK fuzzy rules and hybrid SVD-based learning , 2002, 2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proceedings (Cat. No.02CH37291).

[51]  Kazuo Tanaka,et al.  Modeling and control of carbon monoxide concentration using a neuro-fuzzy technique , 1995, IEEE Trans. Fuzzy Syst..