A Vectorization-Optimization-Method-Based Type-2 Fuzzy Neural Network for Noisy Data Classification

This paper proposes a vectorization-optimization-method (VOM)-based type-2 fuzzy neural network (VOM2FNN) for noisy data classification. In handling problems with uncertainties, such as noisy data, type-2 fuzzy systems usually outperform their type-1 counterparts. Hence, type-2 fuzzy sets are adopted in the antecedent parts to model the uncertainty. To consider the classification problems, the discriminative capability is crucial to determine the performance. Therefore, a VOM is proposed in the consequent parts to increase the discriminability and reduce the parameters. Compared with other existing fuzzy neural networks, the novelty of the proposed VOM2FNN is its consideration of both uncertainty and discriminability. The effectiveness of the proposed VOM2FNN is demonstrated by three classification problems. Experimental results and theoretical analysis indicate that the proposed VOM2FNN performs better than the other fuzzy neural networks.

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

[2]  Zhang Yi,et al.  A Family of Fuzzy Learning Algorithms for Robust Principal Component Analysis Neural Networks , 2010, IEEE Transactions on Fuzzy Systems.

[3]  Chin-Teng Lin,et al.  LDA-Based Clustering Algorithm and Its Application to an Unsupervised Feature Extraction , 2011, IEEE Transactions on Fuzzy Systems.

[4]  Jeih-Weih Hung,et al.  Comparative analysis for data-driven temporal filters obtained via principal component analysis (PCA) and linear discriminant analysis (LDA) in speech recognition , 2001, INTERSPEECH.

[5]  Zhaohong Deng,et al.  Robust Relief-Feature Weighting, Margin Maximization, and Fuzzy Optimization , 2010, IEEE Transactions on Fuzzy Systems.

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

[7]  Yixin Chen,et al.  Support vector learning for fuzzy rule-based classification systems , 2003, IEEE Trans. Fuzzy Syst..

[8]  Jung-Hsien Chiang,et al.  Support vector learning mechanism for fuzzy rule-based modeling: a new approach , 2004, IEEE Trans. Fuzzy Syst..

[9]  Jeih-Weih Hung,et al.  Optimization of temporal filters for constructing robust features in speech recognition , 2006, IEEE Transactions on Audio, Speech, and Language Processing.

[10]  Chia-Feng Juang,et al.  Reinforcement Interval Type-2 Fuzzy Controller Design by Online Rule Generation and Q-Value-Aided Ant Colony Optimization , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[11]  Jerry M. Mendel,et al.  On the Stability of Interval Type-2 TSK Fuzzy Logic Control Systems , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[12]  Jia Zeng,et al.  Type-2 fuzzy Gaussian mixture models , 2008, Pattern Recognit..

[13]  Chin-Teng Lin,et al.  GA-based noisy speech recognition using two-dimensional cepstrum , 2000, IEEE Trans. Speech Audio Process..

[14]  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.

[15]  Anastasios Tefas,et al.  Weighted Piecewise LDA for Solving the Small Sample Size Problem in Face Verification , 2007, IEEE Transactions on Neural Networks.

[16]  Gin-Der Wu,et al.  A Maximizing-Discriminability-Based Self-Organizing Fuzzy Network for Classification Problems , 2010, IEEE Transactions on Fuzzy Systems.

[17]  Jerry M. Mendel,et al.  Computing derivatives in interval type-2 fuzzy logic systems , 2004, IEEE Transactions on Fuzzy Systems.

[18]  Pong C. Yuen,et al.  Incremental Linear Discriminant Analysis for Face Recognition , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[19]  Jing Peng,et al.  Discriminant Learning Analysis , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[20]  Alain Biem,et al.  Minimum classification error training for online handwriting recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Jia Zeng,et al.  Type-2 fuzzy hidden Markov models and their application to speech recognition , 2006, IEEE Transactions on Fuzzy Systems.

[22]  Darrell A. Turkington.  Matrix Calculus and Zero-One Matrices: Statistical and Econometric Applications , 2001 .

[23]  Giuseppe Pirlo,et al.  Fuzzy-Zoning-Based Classification for Handwritten Characters , 2011, IEEE Transactions on Fuzzy Systems.

[24]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[25]  Chunyan Miao,et al.  An Extension to Fuzzy Cognitive Maps for Classification and Prediction , 2011, IEEE Transactions on Fuzzy Systems.

[26]  Chia-Feng Juang,et al.  An Interval Type-2 Fuzzy-Neural Network With Support-Vector Regression for Noisy Regression Problems , 2010, IEEE Transactions on Fuzzy Systems.

[27]  Jerry M. Mendel,et al.  Type-2 fuzzy sets made simple , 2002, IEEE Trans. Fuzzy Syst..

[28]  Wu Chou,et al.  Discriminative learning in sequential pattern recognition , 2008, IEEE Signal Processing Magazine.

[29]  Gin-Der Wu,et al.  A TS-Type Maximizing-Discriminability-Based Recurrent Fuzzy Network for Classification Problems , 2011, IEEE Transactions on Fuzzy Systems.

[30]  Chia-Feng Juang,et al.  A Self-Organizing TS-Type Fuzzy Network With Support Vector Learning and its Application to Classification Problems , 2007, IEEE Transactions on Fuzzy Systems.

[31]  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.

[32]  Xiaowei Yang,et al.  A Kernel Fuzzy c-Means Clustering-Based Fuzzy Support Vector Machine Algorithm for Classification Problems With Outliers or Noises , 2011, IEEE Transactions on Fuzzy Systems.

[33]  Chia-Feng Juang,et al.  A Type-2 Self-Organizing Neural Fuzzy System and Its FPGA Implementation , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[34]  Hidetomo Ichihashi,et al.  Fuzzy PCA-Guided Robust $k$-Means Clustering , 2010, IEEE Transactions on Fuzzy Systems.

[35]  Faa-Jeng Lin,et al.  Adaptive Control of Two-Axis Motion Control System Using Interval Type-2 Fuzzy Neural Network , 2009, IEEE Transactions on Industrial Electronics.

[36]  Chin-Teng Lin,et al.  Word boundary detection with mel-scale frequency bank in noisy environment , 2000, IEEE Trans. Speech Audio Process..

[37]  Herman J. M. Steeneken,et al.  Assessment for automatic speech recognition: II. NOISEX-92: A database and an experiment to study the effect of additive noise on speech recognition systems , 1993, Speech Commun..

[38]  Jesús Alcalá-Fdez,et al.  A Fuzzy Association Rule-Based Classification Model for High-Dimensional Problems With Genetic Rule Selection and Lateral Tuning , 2011, IEEE Transactions on Fuzzy Systems.

[39]  Yang-Yin Lin,et al.  A Recurrent Self-Evolving Interval Type-2 Fuzzy Neural Network for Dynamic System Processing , 2009, IEEE Transactions on Fuzzy Systems.

[40]  Eyke Hüllermeier,et al.  Top-Down Induction of Fuzzy Pattern Trees , 2011, IEEE Transactions on Fuzzy Systems.

[41]  Chin-Teng Lin,et al.  Support-vector-based fuzzy neural network for pattern classification , 2006, IEEE Transactions on Fuzzy Systems.

[42]  Dieter Geller,et al.  Improvements in connected digit recognition using linear discriminant analysis and mixture densities , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[43]  Hao Ying,et al.  Derivation and Analysis of the Analytical Structures of the Interval Type-2 Fuzzy-PI and PD Controllers , 2010, IEEE Transactions on Fuzzy Systems.

[44]  M. L. Shire,et al.  Data-driven modulation filter design under adverse acoustic conditions and using phonetic and syllabic units , 1999, EUROSPEECH.