By Giuliano Augusti, Marcello Ciampoli (auth.), Adam Korytowski, Kazimierz Malanowski, Wojciech Mitkowski, Maciej Szymkat (eds.)

This ebook is a suite of prolonged types of papers offered on the twenty third IFIP TC 7 convention on procedure Modeling and Optimization, held in Cracow, Poland, in July 2007.

The 29 revised papers have been rigorously chosen from various submissions. They disguise a variety of themes together with optimzation concept; algorithms of nonlinear optimization; discrete optimization; stochastic optimization; dispensed parameter structures: modeling and regulate; optimum keep an eye on; stochastic regulate and fiscal arithmetic; structural layout; balance and sensitivity research in optimum keep watch over; and complicated systems.

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Additional info for System Modeling and Optimization: 23rd IFIP TC 7 Conference, Cracow, Poland, July 23-27, 2007, Revised Selected Papers

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While the difference is small at first, the performance of the present system provides better results than the optimal control theory, because of the robustness of the present system. As time proceeds, the difference of performance becomes larger gradually. This shows that the proposed system can adapt to the change of structural characteristic. Since the present system could identify the change of structure and environment, it switched the controller from the robust one to the adaptive one efficiently.

1. 1 Robust Controller In the robust controller, the control force is calculated by fuzzy-neural network system [2, 3]. Although fuzzy-neural network is one of neural networks [4], it can provide the 38 H. Furuta et al. Fig. 1. Structure of the system Fig. 2. Structure of fuzzy-neural network calculating results equal to the fuzzy reasoning method. Therefore, it has characteristics of both neural network and fuzzy reasoning, namely, learning ability of neural network and robustness of fuzzy reasoning.

2 Adaptive Controller For the adaptive controller, the control force is calculated by fuzzy control with a learning mechanism using the steepest decent method. Since the learning method is the same as for the neural network, this fuzzy control system is called neuro-fuzzy system. The neuro-fuzzy system has also the characteristics of both neural network and fuzzy reasoning that are the learning ability of the neural network and the robustness of the fuzzy reasoning. However, neuro-fuzzy structure is simpler than fuzzy-neural network, and therefore the learning is faster and convergence is generally quick.

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