Please use this identifier to cite or link to this item:
A Simplified and Parsimonious Type-2 Fuzzy Neural Network with Two-Stage Learning and FPGA Implementation
fuzzy neural networks(FNNs)
type-2 fuzzy systems
|引用:|| N. N. Karnik , J. M. Mendel, and Q. Liang, “Type-2 fuzzy logic systems,” IEEE Trans. on Fuzzy Systems, vol. 7, no. 6, pp. 643-658, 1999.  J. M. Mendel and R. I. John, “Type-2 fuzzy sets made simple,” IEEE Trans. On Fuzzy Systems, vol. 10, no. 2, pp. 117-127, 2002.  J. M. Mendel, Uncertain Rule-Based Fuzzy Logic System: Introduction and New Directions, Prentice Hall, Upper Saddle River, NJ2001.  Q. Liang and J. M. Mendel, “Equalization of nonlinear time-varying channels using type-2 fuzzy adaptive filters,” IEEE Trans. Fuzzy systems, vol. 8, no. 551-563, 2000.  H. B. Mitchell, “Pattern recognition using type-2 fuzzy sets,” Information Sciences, vol. 170, pp. 409-418, 2005.  P. Melin and O. Castillo, “Intelligent control of non-linear dynamic plants using type-2 fuzzy logic and neural networks,” Proc. IEEE Int. Conf. Fuzzy Systems, Budapest, Hungary, July, 2004.  H. Hagras, “A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots,” IEEE Trans. Fuzzy Systems, vol. 12, no. 524-539, 2004.  M. Melgarejo and C. Pena-Reyes, “Hardware architecture and FPGA implementation of a type-2 fuzzy system,” Proc. Of Great Lakes Symposium on VLSI (GLSVLSI), Boston, USA, pp. 458-261, 2004.  R. I. John, P. R. Innocent, and M. R. Barnes, “Neuro-fuzzy clustering of radiographic tibia image data using type-2 fuzzy sets,” Information Sciences, vol. 125, pp. 203-220, 2000.  A. G. Luigi Di Lascio and A. Nappi, “Medical differential diagnosis through type-2 fuzzy sets,” Proc. Of IEEE Int. Conf. Fuzzy Systems, pp. 371-376, 2005.  Q. Liang and J. M. Mendel, “Interval type-2 fuzzy logic systems: theory and design,” IEEE Trans. On Fuzzy Systems, vol. 8, no. 5, pp. 535-550, 2000.  C. H. Lee, Y. C. Lin, and W. Y. Lai, “Systems identification using type-2 fuzzy neural network (Type-2 FNN) systems,” Proc. IEEE Int. Symp. Computational Intelligence in Robotics and Automation, vol. 3, pp. 1264-1269, 2003.  C. H. Wang, C. S. Cheng, and T. T. Lee, “Dynamical optimal training for interval type-2 fuzzy neural network (T2FNN),” IEEE Trans. on Syst., , Man, and Cyber. - Part B: Cybernetics, vol. 34, no. 3, pp. 1462-1477, 2004.  J. M. Mendel, “Computing derivatives in interval type-2 fuzzy logic system,” IEEE Trans. On Fuzzy Systems, vol. 12. no. 1, pp. 84-98, Feb. 2004.  H. Hagras, “Comments on dynamical optimal training for interval type-2 fuzzy neural network (T2FNN),” IEEE Trans. Syst., Man and Cyber. - Part B: Cybernetics, vol. 36, no. 5, pp. 1206-1209, Oct. 2006.  C. F. Juang and Y. W. Tsao, “A self-evolving interval type-2 fuzzy neural network with on-line structure and parameter learning,” IEEE Trans. Fuzzy Systems, vol. 16, no. 6, pp. 1411-1424, Dec. 2008.  O. Uncu and . B. , “Discrete interval type-2 fuzzy system models using uncertainty in learning parameters,” IEEE Trans. Fuzzy Systems, vol. 15, no. 1, pp. 90-106, Feb. 2007.  C. F. Juang and C. T. Lin, “An on-line self-constructing neural fuzzy inference network and its applications,” IEEE Trans. Fuzzy Systems, vol. 6. no. 1, pp. 12-32, Feb. 1998.  C. F. Juang and Y. W. Tsao, “A type-2 self-organizing neural fuzzy system and its FPGA implementation”, IEEE Trans. Syst., Man, and Cyber., Part B: Cyber. vol.38, no.6, pp.1537-1548, 2008.  C. J. Lin and C. T. Lin, “An ART-based fuzzy adaptive learning control network,” IEEE Trans. Fuzzy Systems, vol. 5, no. 4, pp. 477-496, Nov. 1997.  M. Sugeno and T. Yasukawa, “A fuzzy logic based approach to qualitative modeling,” IEEE Trans. Fuzzy Syst., vol. 1, no. 1, pp. 7-31, Feb.1993.  H. Nakanishi, I. B. Turks﹐en, and M. Sugeno, “Areviewand comparison of six reasoning methods,” Fuzzy Sets Syst., vol. 57, pp. 257-295, 1993.  D. Kim and C. Kim, “Forecasting time series with genetic fuzzy predictor ensemble,” IEEE Trans. Fuzzy Systems, vol. 5, no. 4, pp. 523-535, 1997.  M. Russo, “Genetic fuzzy learning,” IEEE Trans. Evolutionary Computation, vol. 4, pp. 259-273, 2000.  K. B. Cho and B. H. Wang, “Radial basis function based adaptive fuzzy systems and their applications to system identiﬁcation,” Fuzzy Sets Syst., vol. 83, pp. 325-339, 1996.  J. Kim and N. K. Kasabov, “HyFIS: Adaptive neuro-fuzzy inference systems and their application to nonlinear dynamic systems,” Neural Netw.,vol. 12, pp. 1301-1319, 1999.  D.Nauk andR.Kruse, “Neuro-fuzzy systems for function approximation,”Fuzzy Sets Syst., vol. 101, no. 2, pp. 261-271, 1999.  S.Wu and M. J. Er, “Dynamic fuzzy neural networks—A novel approach to function approximation,” IEEE Trans. Syst., Man, Cybern. B, Cybern.,vol. 30, no. 2, pp. 358-364, Apr. 2000.  M. Russo, “Genetic fuzzy learning,” IEEE Trans. Evol. Comput.,vol.4,no. 3, pp. 259-273, Sep. 2000.  S. Paul and S. Kumar, “Subsethood-product fuzzy neural inference system(SuPFuNIS),” IEEE Trans. Neural Netw., vol. 13, no. 3, pp. 578-599,May 2002.  Y. Gao and M. J. Er, “NARMAX time series model prediction: Feedfor-ward and recurrent fuzzy neural approaches,” Fuzzy Sets Syst., vol. 150,pp. 331-350, 2005.  A. S. Weigend and N. A. Gersehnfield, Time Series Prediction: Forecasting the Future and Understanding the Past, Reading, MA: Addison-Wesley, 1994.  S. Singh, “Noise impact on time-series forecasting using an intelligent pattern matching technique,” Pattern Recognition, vol. 32, no. 8, pp. 1389-1398, Aug. 1999.  J. L. Elman, “Finding structure in time ”, Cognit. Sci., vol.14, pp. 179-211 ,1990.  R. M. Neal, “Bayesian learning for neural networks,” Ph.D. dissertation,Dept. Comput. Sci., Univ. Toronto, Toronto, ON, Canada, 1995.  Jianke Zhu,StevenC.H.Hoi, and Michael Rung-Tsong Lyu, “Robust Regularized Kernel Regression,” IEEE Trans. Syst., Man, and Cyber., Part B: Cyber. vol.38, no.6, pp.1639-1643, Dec, 2008.  W. Chu, S. S. Keerthi, and C. J. Ong, “Bayesian support vector regression using a uniﬁed loss function,” IEEE Trans. Neural Netw., vol. 15, no. 1,pp. 29-44, Jan. 2004.|
This paper proposes a Simplified and Parsimonious Type-2 Fuzzy Neural Network with two-stage learning (SPT2FNN). The antecedent part in each fuzzy rule of SPT2FNN uses interval type-2 fuzzy sets and the consequent part is Takagi-Sugeno-Kang (TSK) type. The SPT2FNN uses a simplified extended-output-calculation operation to reduce the computation time and hardware implementation cost. The initial rule set in the SPT2FNN is empty. The SPT2FNN uses a two-stage learning algorithm to construct interval type-2 fuzzy rules from extension of type-1 fuzzy rules. The objective of the first stage is to construct type-1 fuzzy rules via online structure learning and parameter learning. The second stage first extends the constructed type-1 fuzzy rules to interval type-2 fuzzy rules, where highly overlapped type-1 fuzzy sets are merged to interval type-2 fuzzy sets to reduce the total number of fuzzy sets. This stage then tunes consequent and antecedent parameters in the type-2 fuzzy rules using rule-ordered Kalman filter algorithm and gradient descent algorithm, respectively. SPT2FNN has been applied to simulations on system identification, stock price prediction, chaotic signal prediction, real-time series prediction and the robot arm mapping problems. Comparisons with several type-1 and type-2 fuzzy systems in these examples have verified the effectiveness and efficiency of SPT2FNN. A new hardware circuit is proposed to implement the learned SPT2FNN in an FPGA chip. The simplified function in the SPT2FNN helps to reduce hardware implementation cost.
|Appears in Collections:||電機工程學系所|
Show full item record
TAIR Related Article
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.