類神經模糊系統是由類神經網路和模糊系統結合而成。藉由樣本學習，相較於傳統的模糊系統，此系統不需要專家和有經驗的使用者就能自行調整模糊歸屬函數並產生模糊推論規則。然而，因為需要做模糊運算和疊代學習與訓練，類神經模糊系統通常需要大量的訓練時間。因此，為了工程上之實現，有效率的程式設計和硬體之輔助是必要的。在本論文中，顯示出一五層網路、多進多出和三個步驟學習法則的類神經模糊系統之比模糊控制系統以及類神經控制系統有更具自我學習及透明化之能力。並且，為了更實際之應用，用C程式語言撰寫成的類神經模糊控制器與數位訊號處理器做連結後，能有效的大量降低訓練所耗時間，並成功的應用於智慧型結構做為受控體的系統識別。

　The neuro-fuzzy system by integrating neural network and fuzzy system is known desirable, the system can adjust the fuzzy membership functions and produce the fuzzy inference rules by learning without experts and experiments. However, the algorithm of the neuro-fuzzy system is usually time consuming in the training process. Therefore, an efficient programming and hardware implementation are needed in engineering applications. In this thesis, a MIMO, five layers and three-phase network is presented to show the better performance comparisons than fuzzy system and higher transparency than neural network system both in system identification and vibration control. For more practical application, the system programmed by C is used to implement the off-line identification with the assistant of DSP. It can be seen that the identification process is much less time consuming than by using MATLAB©.

Ahn, T.C., Kwon, Y. W., Hwang, H. S. and Pedrycz, W., “Design of Neuro-fuzzy Controller on DSP for Real-time Control of Induction Motors,” ISFA World Congress and 2nd NAFIPS International Conference, 2001. Joint 9th, 25-28, pp. 3038-3043, Jul. 2001.
Alleyne, A. and Hedrick, J. K., “Nonlinear Adaptive Control of Active Suspensions,” IEEE Transactions on Control Systems Technology, Vol. 3, No. 1, pp. 94-101, 1995.
Alpsan, D., Towsey, M., Ozdamar, O., Tsoi, A. C. and Ghista, D. N., “Efficacy of Modified Backpropagation and Optimaisation Methods on A Real-World Medical Problem,” Neural Networks, Vol. 8, No. 6, pp. 945-962, 1995.
Angeline, P. J., Saunders, G. M. and Pollack, J. B., “An Evolutionary Algorithm that Construct Recurrent Neural Networks,” IEEE Transaction on Neural Networks, Vol. 5, No. 1, pp.54-65, 1994.
Barada, S. and Singh, H., “Generating Optimal Adaptive Fuzzy-Neural Models of Dynamical Systems With Applications to Control,” IEEE Transactions on Systems, Man and Cybernetics, Part C, Vol. 28, No. 3, pp. 371-391, Aug. 1998.
Bhattacharyya, K. and Crow, M.L., “A Fuzzy Logic Based Approach to Direct Load Control,” IEEE Transaction on Power Systems, Vol. 11, No. 2, pp. 708-714, 1996.
Bona, B., Carabelli, S., Chiaberge, M., Miranda, E. and Reyneri, L. M., “A Neuro-Fuzzy Core for DSP-Based Controls of Complex System,” IEEE International Conference on Systems, Man and Cybernetics, Computational Cybernetics and Simulation, , Vol. 4, 12-15, pp. 3522-3527, Oct. 1997.
Chau, K.T., Chung, S. W. and Chan, C. C., “Neuro-Fuzzy Speed Tracking Control of Traveling-Wave Ultrasonic Motor Drives Using Direct Pulsewidth Modulation,” IEEE Transactions on Industry Applications, Vol.39, No.4, pp. 1061-1069, 2004.
Czogala, E. and Pedrycz,W., “On Identification in Fuzzy Systems and its Applications in Control Problem,” Fuzzy Sets and Systems, Vol. 10, pp. 73-8, 1981.
Dote, Y. and Kano, K., “DSP-Based Neuro-Fuzzy Position Controller for Servomotor,” Proceedings of the International conference on, Industrial Electronics, Control, Instrumentation, and Automation, No. 9-13, Nov. 1992.
Dubois, D. and Prade, H., Fuzzy Sets and Systems: Theory and Applications, New York: Academic Press, 1980.
Farag, W. A., Quintana, V. H. and Lambert-Torres, G., “A Genetic-Based Neuro-Fuzzy Approach for Modeling and Control of Dynamical Systems,” IEEE Transaction on Neural Networks, Vol. 9, No. 5, pp. 756-767, 1998.
Fathi-Torbaghan, M. and Hildebrand, L., “Model-Free Optimization of Fuzzy Rule-Based Systems Using Evolution Strategies,” IEEE Transaction on Fuzzy Systems, Man and Cybernetics, Vol. 27, No. 2, pp. 270-277, 1997.
Fei, M. and Ho, S. L., “Progress in On-Line Adapative, Learning and Evolutionary Strategies for Fuzzy Logic Control,” IEEE Conference on Power Electronics and Drive Systems, Vol. 2, pp. 1108-1113, 1999.
Hebb, D. O., The Organization of Behavior: A Neuropsychological Theory. New York: John Wiley.
Higashi, M. and Klir, G. J., “Identification of Fuzzy Relation Systems,” IEEE Transaction on Fuzzy Systems, Man and Cybernetics, Vol. SMC-14, pp. 349-355, 1984.
Hsu, L. S., Teh, H. H., Wang, P. Z., Chan, S. C. and Loe, K. F., “Fuzzy Neural-Logic Systems,” Proceedings International Joint Conference Neural Networks, Vol. 1, pp. 245-250, Baltimore, MD, 1992.
Ichihashi, H., Iterative Fuzzy Modeling and a Hierarchical Network, Proc. 4th IFSA Congr., Brussels, Vol. Eng., pp. 49-52, 1991.
Ishibuchi, H., Fujioka, R. and Tanaka, H.,” Neural Networks That Learn from Fuzzy If-Then Rules,” IEEE Transactions on Fuzzy Systems, Vol. 1, No. 2, pp. 85-91, May 1993.
Jacobs, R. A., “Increased Rates of Convergence through Learning Rate Adaptation,” Neural Networks, Vol. 1, pp. 295-307, 1988.
Jang, J. S., “Self-Learning Fuzzy Controllers Based on the Temporal Back Propagation.” IEEE Transaction on Neural Networks, Vol. 3, No. 5, pp. 714-723, 1992.
Jin, Y., Jiang, J. and Zhu, J., “Neural network Based Fuzzy Identification and its Application to Modeling and Control of Complex Systems,” IEEE Transaction on Fuzzy Systems, Man and Cybernetics, Vol. 25, No. 6, pp. 990-997, 1995.
Jouffe, L., “Fuzzy Inference System Learning by Reinforcement Methods,” IEEE Transaction on Fuzzy Systems, Man and Cybernetics, Vol. 28, No. 3, pp. 338-355, 1998.
Juang, C. F. and Lin, C. T., “An On-Line Self-Constructing Neural Fuzzy Inference Network and Its Applications,” IEEE Transaction on Fuzzy Systems, Vol. 6, No. 1, pp. 12-32, 1998.
Kaufmann, A., Introduction to the Theory of Fuzzy Subsets, New York: Academic Press, 1975.
Keller, J. M., Krishnapuram, R., and Rhee, F. C.-H., “Evidence Aggregation networks for Fuzzy Logic Inference,” IEEE Transaction on Neural Networks, Vol.3, No. 5, pp. 761-769, Sep. 1992.
Klir, G. J. and Folger, T.A., Fuzzy Sets, Uncertainty, and Information. Englewood Cliffs, NJ: Prentice-Hall, 1988.
Kohonen, T., Self-Organization and Associative Memory, Berlin: Springer-Verlag, 1988.
Kukolj, D., and Levi, E., “Identification of Complex Systems Based on Neural and Takagi-Sugeno Fuzzy Model,” IEEE Transactions on Man, and Cybernetics, Part B, Vol. 34, No. 1, Feb. 2004.
Kumbla, K., Akbarzadeh-T., M.-R. and Jamshidi, M., “TMS320 DSP Based Neuro-Fuzzy Controller,” IEEE International conference on Systems, Man and Cybernetics, Intelligent Systems for the 21st Century, Vol. 5, 22-25 Oct., pp. 4015-1020, Oct. 1995.
Lin, C. J., “A GA-based Neural Fuzzy System for Temperature Control.” Fuzzy Sets and Systems. Vol. 143, pp. 311-333, 2004.
Lin, C. T., “A Neural Fuzzy Control System with Structure and Parameter Learning,” Fuzzy Sets and Systems, Vol. 70, No. 2-3, pp. 183-212, 1995.
Lin, C. T. and Lee, C. S. G., “A Multi-Valued Boltzmann Machine,” Proceedings of International Joint Conference on Neural Networks, pp. 2546-2552, Singapore, 1991a.
Lin, C. T. and Lee, C. S. G., Neural Fuzzy Systems: a Neuro-Fuzzy Synergism to Intelligent Systems, Prentice Hall PTR, 1996.
Lin, C. T. and Lee, C. S. G., “Neural-Network-Based Fuzzy Logic Control and Decision System,” IEEE Transaction on Computers, Vol. 40, No. 12, pp.1320-1336, 1991b.
Lin, C. T. and Lu, Y. C., “A Neural Fuzzy System with Fuzzy Supervised Learning,” IEEE Transaction on Fuzzy Systems, Man and Cybernetics, Vol. 26 No. 5, pp. 744-763, 1996.
Lin, Y., Cunningham, G. A. and Coggeshall, S. V., “Using Fuzzy Partitions to Create Fuzzy Systems from Input-Output Data and Set the Initial Weights in a Fuzzy Neural Network,” IEEE Transaction on Fuzzy Systems, Vol. 5, No. 4, pp. 614-621, 1997.
Magoulas, G., Vrahatis, M. N. and Androulakis, G. S., “Effective Backpropagation Training with Variable Stepsize,” Neural Networks, Vol. 10, No. 1, pp. 69-82, 1997.
Mamdani, E. H. and Assilian, S., “An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller,” International Journal Man-Machine Studies, No.1, pp. 1-13, 1975.
Martienzzo, V., “Genetic Evolution of the Topology and Weight Distribution of Neural networks,” IEEE Transaction on Neural Networks, Vol.5, No.1, pp.39-53, 1994.
McCulloch, W. S. and Pitts, W., “A Logical Calculus of Ideas Immanent in Nervous Activity,” Bulletin of Mathematical Biophysics, 5:115-133, 1943. Hebb, D. O., The Organization of Behavior: A Neuropsychological Theory. Ew York: John Wiley.
Nauck, D. and Kruse, R., “A Fuzzy Neural Network Learning Control Rules and Membership Functions by Fuzzy Error Backpropagation,” Proceedings IEEE International Conference on Neural Networks, pp. 1022-1027, 1993.
Nelson, M. M. and Illingworth, W. T., A Practical Guide to Neural Nets. Reading, MA: Addison-Wesley.
Nomura, H., Hayashi, I. and Wakami, N., “A Self-Tuning Method of Fuzzy Control by Descent Method,” in Proceedings of International Fuzzy Systems Association, Bruxelles, pp.155-158. 1991.
Pal, N. R. and Pal, T., “On Rule Pruning Using Fuzzy Neural Networks,” Fuzzy Sets and Systems, Vol. 106, No. 3, pp. 335-347, 1999.
Pao, Y. H., Adaptive Pattern Recognition and Neural Networks, Reading, MA: Addison-Wesley, 1989.
Parker, D. B., “Learning Logic,” Technical Report TR-47, Center for Computational Research in Economics and Management Science, Massachusetts Institute of Technology, Cambridge, MA, 1985.
Pedraycz, W., “An Identification Algorithm in Fuzzy Relational Systems,” Fuzzy Sets and Systems, Vol. 13, pp. 153-167, 1984.
Rosenblatt, F. “The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain,” Psychological Review, Vol. 65, No. 6, pp.386-408, Nov. 1958.
Rumelhart, D. E., Hinton, G. E. and Williams, R. J., “Learning Internal Representation by Propagation Errors,” Parallel Distributed Processing, Cambridge, MA: MIT Press, 1986.
Rumelhart, D. E. and McClelland, J. L., PDP Research Group, Parallel Distributed Processing, MIT Press, Cambridge, MA, 1986.
Shann, J. J. and Fu, H. C., “A Fuzzy Neural Network for Rule Acquiring on Fuzzy Control System,” Fuzzy Sets and Systems, Vol. 71, No. 3, pp. 345-357, 1995.
Shi, Y. and Mizumoto, M., “A New Approach of Neuro-fuzzy Learning Algorithm for Tuning Fuzzy Rules,” Fuzzy Sets and Systems. Vol. 12, No. 1, pp. 99-116, 2000a.
Shi, Y. and Mizunoto, M., “Some Considerations on Conventional Neuro-Fuzzy Learning Algorithms by Gradient Descent Method,” Fuzzy Sets and Systems, Vol. 112, pp. 51-63, 2000b.
Siddique, M. N. H. and Thoki, M. O., “GA-Based Neuro-Fuzzy Controller for Flexible-link Manipulator,” Proceedings of the 2002 International Conference on Control Applications, Vol. 1, pp. 471 - 47, Sep. 2002.
Soo, Y. Y. and Myung, J. C., “A Robust Fuzzy Logic Controller for Robot Manipulators with Uncertainties,” IEEE Transaction on Fuzzy Systems, Man and Cybernetics, Vol. 27, No. 4, pp. 706-713, 1997.
Su, M. C., Liu, C. W. and Tsay, S. S., ”Neural-Network-Based Fuzzy Model and its Application to Transient Stability Prediction in Power Systems,” IEEE Transaction on Fuzzy Systems, Man and Cybernetics, Vol. 29, No. 1, pp. 149-157, 1999.
Sunwoo, M., Cheok, K. C. and Huang, N. J., ”Model Reference Adaptive Control for Vehicle Active Suspension Systems,” IEEE Transaction on Industrial Electronics, Vol. 38, No. 3, pp.217-222, 1991.
Takagi, H., Suzuki, N., Koda, T. and Kojima, Y., “Neural Networks Designed on Approximate Reasoning Architecture and Their Applications,“ IEEE Transaction on Neural Networks, Vol. 3, No. 5, pp. 752-760, 1992.
Takagi, T. and Sugeno, M., “Derivation of Fuzzy Control Rules from Human Operator’s Control Actions,” in Proceedings of IFAC Symp. Fuzzy Information, Knowledge Representation and Decision Analysis, pp. 55-60, 1983.
Takagi, T., and Sugeno, M., “Fuzzy Identification of Systems and Its Applications to Modeling and Control,” IEEE Transaction on Fuzzy Systems, Man and Cybernetics, Vol. AMC-15, pp. 116-132, 1985.
Teng-Tiow, T. and Surya, W. T., “Fuzzy System as Parameter Estimator of Nonlinear Dynamic Functions,” IEEE Transaction on Fuzzy Systems, Man and Cybernetics, Vol. 27, No. 2, pp. 313-326, 1997.
Tung, Y. R. and Yang, S. M., “Development of a Neuro-Fuzzy System on Modeling and Applications”, Master Thesis, National Cheng Kung University, Tainan, Taiwan R.O.C, Jul. 2001.
Wang, L. and Langari, R., “Complex Systems Modeling via Fuzzy Logic,” IEEE Transaction on Systems, Man and Cybernetics, Vol. 26, No. 1, pp. 100-106, 1996.
Werbos, P. J., “Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences,” Dotoral Dissertation, Harvard University, Cambridge, MA, 1974.
Yang, S. M. and Lee, G. S., “Vibration Control of Smart Structures by Using Neural Networks,” Journal of Dynamic Systems, Measurement, and Control, ASME, Vol. 119, No. 1, pp. 34-39, 1997.
Yu, X. H. and Chen, G. A., “Efficient Backpropagation Learning Using Optimal Learning Rate and Momentum,” Neural Networks, Vol. 10, No. 3, pp. 517-527, 1997.
Yu, X. H., Chen, G. A. and Cheng, S. X., “Dynamic Learning Rate Optimization of the Backpropagation Algorithm,” IEEE Transaction on Neural Networks, Vol. 6, No. 3, pp. 669-677, 1995.
Zadeh, L. A., “Fuzzy Sets,” Information and Control, Vol. 8, pp. 338-353, 1965.
Zadeh, L. A., “Outline of A New Approach to The Analysis of Complex Systems and Decision Processes,” IEEE Transaction on Systems, Man and Cybernetics, Vol. 1, pp. 28-44, 1973.
Zang, J. Q., Ovaska, S. J. and Gao, X. Z., “A Novel MIMO Fuzzy Model,” IEEE Southeastcon 1999 Proceedings, pp. 21-24, March 1999.
Zhai, D., Li, L. and Jin, F., “Fuzzy Neural Network for Nonlinear-Systems Model Identification,” Computational Intelligence in Robotics and Automation, Proceedings IEEE International Symposium on, Vol. 3, pp. 1282 – 1287, July 2003.
Zhang, J. and Morris, A. J., “Fuzzy Neural Networks for Nonlinear Systems Modeling,” Control Theory and Applications, IEEE Proceedings-, Vol. 142, No. 6, pp. 551-561, Nov. 1995.
Zhang, Y. Q. and Kandel, A., “Compensatory Neurofuzzy Systems with Fast Learning Algorithms,” IEEE Transaction on Neural Networks, Vol. 9, No. 1, pp. 83-105, 1998.
Zimmermann, H. J., Fuzzy Set Theory and Its Applications, 2nd rev. ed. Boston: Kluwer Academic Publishers, Dordrecht, Boston, MA, 1991.