AsiaSim 2007: Asia Simulation Conference 2007, Seoul, Korea, by Jin Woo Park, Tag-Gon Kim, Yun-Bae Kim

By Jin Woo Park, Tag-Gon Kim, Yun-Bae Kim

This e-book contains chosen papers of the Asia Simulation convention 2007, held in Seoul, Korea, October 10-12, 2007.

The forty two revised complete papers offered have been conscientiously reviewed and chosen from one hundred twenty submissions; after the convention, the papers went via one other around of revision. The papers are geared up in topical sections on production, Numerical Simulation I, basic software, Agent established Simulation, Aero house, procedure Dynamics, Numerical Simulation II, internet established Simulation, common Engineering, Logistics Simulation, Simulation and AI, Simulation instruments, Statistical tools, future health Care/Education in addition to method.

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Additional info for AsiaSim 2007: Asia Simulation Conference 2007, Seoul, Korea, October 10-12, 2007, Proceedings (Communications in Computer and Information Science)

Sample text

Middle layer is composed of many judgment intervals. For any input, only a few intervals’ outputs are nonzero. Generalization parameter C stipulates the region size which influences network output. (2) Adopt hashing mapping which uses remainder-divided method, this can project input swatch to the addresses of concept memory. It is divided by a number, and then we can obtain a remainder as actual memory address. The “conflict” problem caused by hashing mapping can be ignored in algorithm design, if parameter is chosen appropriately.

I 44, 891–904 (1997) 12. Tamasevicius: Reproducible analogue circuit for chaotic synchronization. Electron. Lett. 33, 1105–1106 (1997) 13. : A unified approach to controlling chaos via LMI-based fuzzy control system design. IEEE Trans. Circ. Syst. I 45, 1021–1040 (1998) 14. : Design of Adaptive Sliding Mode Controller for Chaos Synchronization with Uncertaintie. Chaos Solitons & Fractals 22, 341–347 (2004) 15. : Fuzzy Sliding Mode Control for A Class of Chaos Synchronization with Uncertainties.

This method could shorten transient response time clearly. The simulation experiment has proved the method can improve control precision and the response rapidness. Keywords: CMAC, flight simulator, RBF, servo system. 1 Introduction Multi-layer feed-forward neural network is the network model which is studied and used most widely in the control field, it use the error back-propagation(BP) method to adjust weights. In theory feed-forward BP networks with more than three layers can approach to any nonlinear functions, so we can use it to identify dynamic model of plant and structure controller.

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