Application and Theory of Petri Nets 1997: 18th by Javier Esparza, Stephan Melzer (auth.), Pierre Azéma,

By Javier Esparza, Stephan Melzer (auth.), Pierre Azéma, Gianfranco Balbo (eds.)

This e-book constitutes the refereed court cases of the 18th foreign convention at the program and concept of Petri Nets, ICATPN'97, held in Toulouse, France, in June 1997.
The 22 revised complete papers offered within the quantity have been chosen from a complete of sixty one submissions; additionally incorporated are 3 invited contributions. All proper themes within the sector are addressed. along with quite a few Petri web periods, workflow administration, telecommunication networking, constraint pride, application semantics, concurrency, and temporal common sense are one of the issues addressed.

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Additional resources for Application and Theory of Petri Nets 1997: 18th International Conference, ICATPN'97 Toulouse, France, June 23–27, 1997 Proceedings

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3. (a)Two-spirals data set and memory cells (b) Chainlink data set and memory cells According to the experimental results, it is hopeful for us to see the system reached 100% classification accuracy with remarkable small number of network units. When it comes to the processing time of the system, it was able to form memory units in 4 sec for Two-spirals and 11 sec for Chainlink data set in Matlab 6,5 programming language conducted on Intel Pentium II processor computer. This is also an advantage of the system with regard to the aiNet’s processing time 14 sec and 23 sec respectively for the same problems conducted on the same computer.

Joint position and references are shown in (a), (b), and (c); Joint speed and references are shown in (d), (e), and (f) Model Based Intelligent Control of a 3-Joint Robotic Manipulator 39 Fig. 6. The results of the neural network controller. Joint position and references are shown in (a), (b), and (c). Joint speed and references are shown in (d), (e), and (f) 5 Conclusions Through the simulation studies, it was found that robotic manipulators can be controlled by learning. In this study, GPC algorithm was modeled by using artificial neural networks.

These selected artificial data sets are Two-spirals and Chainlink data sets which were used in the performance analysis of aiNet [4]. In the analyses, classification accuracy and memory cell number are given with respect to the suppression parameter (supp or s) in tabulated and graphical forms. Also, compression rates are determined according to the memory cell number (m) that represents the input data set and compared with the compression rates reached by the aiNet algorithm for same data sets.

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