Abstract
The basic components of Soft Computing were almost completely developed by the sixties. In our days SC means either separate or integrated application of Neural Networks (NN) and Fuzzy Systems (FS) enhanced with high parallelism of operation and supported by several deterministic, stochastic or combined parameter-tuning methods (learning). The main advantage of using FS is evading the development of intricate analytical system models.
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References
I. Kováčová, L. Madarász, D. Kováč, J. Vojtko. Neural network linearization of pressure force sensor transfer characteristic. Proc. 8th IEEE Int. Conf. on Intelligent Engineering Systems INES, ISBN 973-662-120-0, 2004, pp. 79–82.
J. Vaščák, L. Madárasz. The design of the evaluated linguistic approximation. Bulletins for Applied Mathematics, Technical University of Budapest and Technical University of Košice, October 1995, pp. 247–250.
R. Reed. Pruning algorithms-a survey. IEEE Trans. on Neural Networks, vol. 4, no. 5, 1993, pp. 740–747.
S. Fahlmann, C. Lebiere. The cascade-correlation learning architecture. Advances in Neural Information Processing Systems, vol. 2, 1990, pp. 524–532.
T. Nabhan, A. Zomaya. Toward generating neural network structures for function approximation. Neural Networks, vol. 7, 1994, pp. 89–91.
G. Magoulas, N. Vrahatis, G. Androulakis. Effective backpropagation training with variable stepsize. Neural Networks, vol. 10, 1997, pp. 69–82.
W. Kinnenbrock. Accelerating the standard backpropagation method using a genetic approach. Neurocomputing, vol. 6, 1994, pp. 583–588.
A. Kanarachos, K. Geramanis. Semi-stochastic complex neural networks. Proc. of IFAC-CAEA’ 98 Control Applications and Ergonomics in Agriculture, 1998, pp. 47–52.
D. Tikk, P. Baranyi, T.D. Gedeon, L. Muresan. Generalization of the rule interpolation method resulting always in acceptable conclusion. Tatra Mountains Math. Publ., vol. 21, 2001, pp. 73–91.
J. Vaščák, L. Madarász. Similarity relations in diagnosis fuzzy systems. Journal of Advanced Computational Intelligence, vol. 4, no. 4, 2000, pp. 246–250.
D. Tikk, Gy. Biró, T.D. Gedeon, L.T. Kóczy, J.D. Yang. Improvements and critique on Sugeno’s and Yasukawa’s qualitative modeling. IEEE Trans. on Fuzzy Systems, vol. 10, no. 5, 2002, pp. 596–606.
J.K. Tar, I.J. Rudas, J.F. Bitó. Group theoretical approach in using canonical transformations and symplectic geometry in the control of approximately mod eled mechanical systems interacting with unmodelled environment. Robotica, vol. 15, 1997, pp. 163–169.
V.I. Arnold. Mathematical Methods of Classical Mechanics. Műszaki Könyvkiadó, Budapest 1985 (in Hungarian).
J.K. Tar, I.J. Rudas, J.F. Bitó, K.R. Kozłowski. A modified renormalization algorithm based adaptive control guaranteeing complete stability for a wide class of physical Systems. In: Elmenreich W., Tenreiro Machado J.A., Rudas I.J. (Eds.) Intelligent Systems at the Service of Mankind, UBOOKS, Augsburg 2004.
E. Miletics, G. Molnárka. Taylor series method with numerical derivatives for initial value problems. Journal of Computational Methods in Sciences and Engineering, vol. 3, no. 3, 2003, pp. 319–329.
E. Miletics. Energy conservative algorithm for numerical solution of initial value problems of hamiltonian systems. Proc. IEEE Int. Conf. on Computational Cybernetics ICCC, ISBN 963 7154 18 3, 2003, pp. 1–4.
J.K. Tar, I.J. Rudas, J.F. Bitó, K. Jezernik. A generalized Lorentz group-based adaptive control for DC drives driving mechanical components. Proc. 10th Int. Conf. on Advanced Robotics ICAR, ISBN 963 7154 05 1, 2001, pp. 299–305.
Y. El Hini. Comparison of tha application of the sympletic and the partially stretched orthogonal transformations in a new branch of adaptive control for mechanical devices. Proc. 10th Int. Conf. on Advanced Robotics ICAR, ISBN 963 7154 05 1, 2001, pp. 701–706.
J.K. Tar, A. Bencsik, J.F. Bitó, K. Jezernik. Application of a new family of symplectic transformations in the adaptive control of mechanical systems, Proc. 28th Annual Conf. of the IEEE Industrial Electronics Society, ISBN 0-7803-7474-6, 2002, pp. 1499–1504.
J.K. Tar, I.J. Rudas, L. Horváth, K. Kozłowski. Analysis of the effect of backlash and joint acceleration measurement noise in the adaptive control of electro-mechanical systems. Proc. IEEE Int. Symp. on Industrial Electronics ISIE, ISBN 0-7803-7912-8 (CD issue), file BF-000965.pdf, 2003.
R. Lozano, I. Fantoni, D.J. Block. Stabilization of the inverted pendulum around its homoclinic orbit. Systems & Control Letters, vol. 40, no. 3, 2000, pp. 197–204.
J.K. Tar, I.J. Rudas, L. Horváth, S.G. Tzafestas. Adaptive control of the double inverted pendulum based on novel principles of soft computing. Proc. Int. Conf. in Memoriam John von Neumann, ISBN 963 7154213, 2003, pp. 257–268.
T. Roska. Development of kilo real-time frame rate TeraOPS computational capacity topographic microprocessors. Plenary Lecture at 10th Int. Conf. on Advanced Robotics ICAR, 2001, Budapest, Hungary, August 22–25, 2001.
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Tar, J.K., Rudas, I.J., Szeghegyi, Á., Kozłowski, K. (2006). Novel Adaptive Control of Partially Modeled Dynamic Systems. In: Kozłowski, K. (eds) Robot Motion and Control. Lecture Notes in Control and Information Sciences, vol 335. Springer, London. https://doi.org/10.1007/978-1-84628-405-2_6
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DOI: https://doi.org/10.1007/978-1-84628-405-2_6
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