Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/9137
標題: 使用權重邊界的簡化式第二型類神經模糊系統以加速運算與FPGA實現
Reduced Interval Type-2 Neural Fuzzy System Using Weighted Bound Set Boundaries for Computation Speedup and FPGA Implementation
作者: 莊凱傑
Juang, Kai-Jie
關鍵字: 模糊類神經系統
Neural fuzzy systems
第二類型模糊系統
類型降低
可解釋的模糊系統
可區分的模糊集合
type-2 fuzzy systems
type reduction
interpretable fuzzy systems
distinguishable fuzzy sets
出版社: 電機工程學系所
引用: [1] J. S. Jang, “ANFIS: Adaptive-network-based fuzzy inference system,” IEEE Trans. Syst., Man, Cybern., vol. 23, no. 3, pp. 665–685, May 1993. [2] 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. [3] N. K. Kasabov and Q. Song, “DENFIS: Dynamic evolving neural-fuzzy inference system and its application for time-series prediction,” IEEE Trans. on Fuzzy Systems, vol. 10, no. 2, pp. 144 -154, April 2002. [4] P.P. Angelov and D. P. Filev, “An approach to online identification of Takagi-Sugeno fuzzy models,” IEEE Trans. on Systems, Man and Cybernetics, Part B: Cybernetics, vol. 34, no. 1, pp. 484-498, 2004. [5] E. D. Lughofer, “FLEXFIS: A robust incremental learning approach for evolving Takagi-Sugeno fuzzy models,” IEEE Trans. Fuzzy Systems, vol. 16, no. 6, pp. 1393-1410, Dec. 2008. [6] J. D. Rubio, “SOFMLS: Online self-organizing fuzzy modified least-squares network,” IEEE Trans. Fuzzy Systems, vol. 17, no. 6, pp. 1296-1309, Dec. 2009. [7] C. F. Juang, T. C. Chen, and W. Y. Cheng, “Speedup of implementing fuzzy neural networks with high-dimensional inputs through parallel processing on graphic processing units,” IEEE Trans. Fuzzy Systems, vol. 19, no. 4, pp. 717-728, Aug. 2011. [8] P. Angelov, “Fuzzily connected multi-model systems evolving autonomously from data streams,” IEEE Trans. Syst., Man, and Cyber. -Part B, Cyber., vol.41, no. 4, pp. 898-910, Aug. 2011. [9] J. M. Mendel, Uncertain Rule-Based Fuzzy Logic System: Introduction and New Directions, Prentice Hall, Upper Saddle River, NJ2001 [10] H. Hagras, “A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots,” IEEE Trans. Fuzzy Systems, vol. 12, no. 524-539, 2004. [11] C. F. Juang and C. H. Hsu, “Reinforcement ant optimized fuzzy controller for mobile-robot wall-following control,” IEEE Trans. Industrial Electronics, vol. 56, no. 10, pp. 3931-3940, Oct. 2009. [12] 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. [13] J. Zeng and Z. Q. Liu, “Type-2 fuzzy hidden Markov models and their application to speech recognition,” IEEE Trans. Fuzzy Systems, vol. 14, no. 3, pp. 454-467, June 2006. [14] 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. [15] 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. [16] J. R. Castro, O. Castillo, P. Melin, A. Rodriguez-Diaz, “A hybrid learning algorithm for a class of interval type-2 fuzzy neural networks,” Information Sciences, vol. 179, pp. 2175-2193, 2009. [17] O. U ncu 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. [18] 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. [19] 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: Cybernetics, vol. 38, no. 6, pp. 1537-1548, Dec. 2008. [20] S. M. Zhou, J. M. Garibaldi, R. I. John, and F. Chiclana, “On constructing parsimonious type-2 fuzzy logic systems via influential rule selection,” IEEE Trans. Fuzzy Systems, vol. 17, no. 3, pp. 654-667, June 2009. [21] J.V. de Oliveira, “Semantic constraints for membership function optimization,” IEEE Trans. Syst. Man, and Cyber., Part A: Systems and Humans, vol. 29, no. 1, pp. 128-138, Jan. 1999. [22] Y. Jin, “Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement,” IEEE Trans. Fuzzy Systems, vol. 8, no. 2, pp. 212-221, 2000. [23] R. Paiva and A. Dourado, “Interpretability and learning in neuro-fuzzy systems,” Fuzzy Sets and Systems, vol. 147, no. 1, pp. 17-38, 2004. [24] S. M. Zhou and J. Q. Gan, “Low-level interpretability and high-level interpretability: A unified view of data-driven interpretable fuzzy system modeling,” Fuzzy Sets Syst., vol. 159, no. 23, pp. 3091–3131, Dec. 2008. [25] M. J. Gacto, R. Alcala, and F. Herrera, “Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures,” Information Sciences, vol. 181, pp. 4340-4360, 2011. [26] M. A. Melgarejo, R. A. Garcia, and C. A. Pena-Reyes, “Pro-Two: a hardware based platform for real time type-2 fuzzy inference,” Proc. IEEE Int. Conf. Fuzzy Systems, vol. 2, pp. 977-982, July 2004. [27] M. A. Melgarejo and C. A. Pena-Reyes, “Implementing interval type-2 fuzzy processors,” IEEE Computational Intelligence Magazine, vol. 2, no. 1, pp. 63-71, 2007. [28] C. F. Juang, Y. Y. Lin, and R. B. Huang, “Dynamic system modeling using a recurrent interval-valued fuzzy neural network and its hardware implementation,” Fuzzy Sets and Systems, vol. 179, no. 1, pp. 83-99, Sep. 2011. [29] H. Wu and J. M. Mendel, “Uncertainty bounds and their use in the design of interval type-2 fuzzy logic systems,” IEEE Trans. Fuzzy Systems, vol. 10, no. 5, pp. 622-639, Oct. 2002. [30] M. J. Patyra, J.L. Grantner, and K. Koster, “Digital fuzzy logic controller: design and implementation,” IEEE Trans. Fuzzy Syst., vol. 4, no. 4, pp. 439–459, 1996. [31] G. Ascia, V. Catania, and M. Russo, “VLSI hardware architecture for complex fuzzy system,” IEEE Trans. Fuzzy Systems, vol. 7, no. 5, pp.553-569, Oct. 1999. [32] C. F. Juang and J. S. Chen, “Water bath temperature control by a recurrent fuzzy controller and its FPGA implementation,” IEEE Trans. Industrial Electronics, vol. 53, no. 3, pp. 941-949, June 2006. [33] D. Deng and N. Kasabov, “Evolving self-organizing maps for online learning, data analysis and modeling,” in Proc. IJCNN’2000 Neural Networks, Neural Comput.: New Challenges Perspectives New Millennium, vol. VI, S.-I. Amari, C. L. Giles, M. Gori, and V. Piuri, Eds., New York, NY, 2000, pp. 3–8. [34] Y. Q. Zhang, B. Jin, and Y. Tang, “Granular neural networks with evolutionary interval learning,” IEEE Trans. Fuzzy Systems, vol. 16, no. 2, pp. 309-319, April 2008. [35] S. M. Chen and Y. C. Chang, “Weighted fuzzy rule interpolation based on GA-based weight- learning techniques,” IEEE Trans. Fuzzy Syst., vol. 19, no. 4, pp. 729-744, Aug. 2011. [36] P. Baranyi, L. T. Koczy, and T. D. Gedeon, “A generalized concept for fuzzy rule interpolation,” IEEE Trans. Fuzzy Syst., vol. 12, no. 6, pp. 820–832, Dec. 2004. [37] Z. H. Huang and Q. Shen, “Fuzzy interpolation and extrapolation: A practical approach,” IEEE Trans. Fuzzy Syst, vol. 16, no. 1, pp. 13–28, Feb. 2008.
摘要: This thesis proposes a reduced interval type-2 neural fuzzy system using weighted bound-set boundaries (RIT2NFS-WB) for simplification of type-reduction operation. The objective of this simplification is to reduce system training time in software implementation and chip size in hardware implementation, especially when the number of rules is large. The antecedent part in the RIT2NFS-WB uses interval type-2 fuzzy sets (IT2FSs) and the consequent can be of Takagi-Sugeno-Kang (TSK) or Mamdani type. The RIT2NFS-WB is built through online structure and parameter learning. In addition to model accuracy, interpretability of the RIT2NFS-WB is improved via considering distributions of the IT2FSs in the input variables. A distinguishability-oriented cost function is used in parameter learning to improve semantics-based interpretability and highly-overlapped IT2FSs are merged to reduce the number of IT2FSs and improve complexity-based interpretability. The software-implemented TSK-type RIT2NFS-WB is hardware implemented on a field-programmable gate array (FPGA) chip. The chip is characterized with online learning ability for TSK-type consequent and weighting parameters update based on the gradient descent algorithm. To accelerate the chip execution speed, the chip utilizes not only the parallel execution properties of fuzzy rules and bound-set boundaries but also the pipeline technique. In particular, flexibility of the chip is considered so that no re-design of the circuits is required when the RIT2NFS-WB is applied to different problems. Characteristics of RIT2NFS-WB and its hardware implementations are verified through various examples and comparisons with various type-1 and interval type-2 fuzzy models
URI: http://hdl.handle.net/11455/9137
其他識別: U0005-1308201214421600
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-1308201214421600
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