Artifical Intelligence and Soft Computing: 10th - download pdf or read online

By Igor Aizenberg (auth.), Leszek Rutkowski, Rafał Scherer, Ryszard Tadeusiewicz, Lotfi A. Zadeh, Jacek M. Zurada (eds.)

ISBN-10: 3642132316

ISBN-13: 9783642132315

ISBN-10: 3642132324

ISBN-13: 9783642132322

This quantity constitutes the lawsuits of the tenth overseas convention on man made Intelligence and delicate Computing, ICAISC’2010, held in Zakopane, Poland in June 13-17, 2010. The articles are equipped in topical sections on Fuzzy platforms and Their functions; information Mining, category and Forecasting; picture and Speech research; Bioinformatics and scientific functions (Volume 6113) including Neural Networks and Their purposes; Evolutionary Algorithms and Their purposes; Agent approach, Robotics and regulate; quite a few difficulties aof synthetic Intelligence (Volume 6114).

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Extra resources for Artifical Intelligence and Soft Computing: 10th International Conference, ICAISC 2010, Zakopane, Poland, June 13-17, 2010, Part II

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626–629. IEEE Computer Society Press, Los Alamitos (2000) 14. : A New Design Method for the ComplexValued Multistate Hopfield Associative Memory. IEEE Trans. Neural Networks 14(4), 891–899 (2003) 15. : Rotation-Invariant Image Association for Endoscopic Positional Identification Using Complex-Valued Associative Memories. G. ) IWANN 2001. LNCS, vol. 2085, pp. 369–374. Springer, Heidelberg (2001) 16. : Improving the capacity of complex-valued neural networks with a modified gradient descent learning rule.

A sector whose border is closest to the current weighted sum z is chosen as a desired sector q. Then either of learning rules (4) or (5) can be applied. 4 Conclusions We observed in this paper in detail complex-valued neurons with the phase-dependent activation functions – the multi-valued neuron and the universal binary neuron. Acknowledgement This work is supported by the National Science Foundation under Grant 0925080. References 1. : Complex-Valued Neural Networks. Springer, Heidelberg (2006) 2.

5. Evidently, for k=2 function (10) coincides with function (9) and in this case the MVN-P coincides with the UBN. The most important property of the MVN-P is its ability to learn those k-valued input/output mappings, which are not threshold in k-valued logic and cannot be learned using a single MVN with activation function (2), but which are partially defined threshold functions in m-valued logic. Complex-Valued Neurons with Phase-Dependent Activation Functions Fig. 5. Geometrical interpretation of the periodic activation function (10) 9 For example, such a multiple-valued function, as a mod k addition of n kvalued variables (a multiplevalued analog of the Parity n function) cannot be learned by a single MVN with activation function (2), but can be learned by a single MVN-P with activation function (10) [5].

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Artifical Intelligence and Soft Computing: 10th International Conference, ICAISC 2010, Zakopane, Poland, June 13-17, 2010, Part II by Igor Aizenberg (auth.), Leszek Rutkowski, Rafał Scherer, Ryszard Tadeusiewicz, Lotfi A. Zadeh, Jacek M. Zurada (eds.)


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