Engineering Applications of Neural Networks: 16th by Lazaros Iliadis, Chrisina Jayne

By Lazaros Iliadis, Chrisina Jayne

This e-book constitutes the refereed complaints of the sixteenth foreign convention on Engineering functions of Neural Networks, EANN 2015, held in Rhodes, Greece, in September 2015.

The 36 revised complete papers awarded including the abstracts of 3 invited talks and tutorials have been rigorously reviewed and chosen from eighty four submissions. The papers are equipped in topical sections on industrial-engineering purposes of ANN; bioinformatics; clever clinical modeling; life-earth sciences clever modeling; learning-algorithms; clever telecommunications modeling; fuzzy modeling; robotics and regulate; shrewdpermanent cameras; development recognition-facial mapping; type; monetary clever modeling; echo kingdom networks.

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Additional resources for Engineering Applications of Neural Networks: 16th International Conference, EANN 2015, Rhodes, Greece, September 25-28 2015.Proceedings

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Intelligent Systems (2013) 9. : The “echo state” approach to analyzing and training recurrent neural networks. Technical report, Fraunhofer Institute for Autonomous Intelligent Systems (2010) 10. : Toward efficient full automatic operation of the PWR steam generator with water level adaptive control. In: Proc. Int. Conf. Boiler Dynamics Contr. , pp. 309–329 (1980) 11. : Steam generator level control with an observer-based algebraic approach. : 8th Int. Conf. in Electrical and Electronics Engineering (ELECO), pp.

Springer International Publishing Switzerland 2015 L. Iliadis and C. ): EANN 2015, CCIS 517, pp. 24–34, 2015. 1007/978-3-319-23983-5_3 On-line Surface Roughness Prediction in Grinding Using Recurrent Neural Networks 25 Using acoustic emission (AE) sensor, in [4] a multi-layer perceptron with 3 hidden layers was used to predict the surface roughness. A multi-layer perceptron Neural Network was also used in [5] in order to predict the surface finish in creep-feed grinding. Also for creep-feed grinding, in a more recent work [6], an Artificial Neural Network (ANN) was presented for the prediction of surface roughness.

Neural Networks: a Comprehensive Foudantion, 2nd edn. Prentice Hall (1998) 8. : A tutorial on training recurrent n. networks, covering bppt, rtrl, and the echo state network approach. Tech. report, Fraunhofer Inst. for Aut. Intelligent Systems (2013) 9. : The “echo state” approach to analyzing and training recurrent neural networks. Technical report, Fraunhofer Institute for Autonomous Intelligent Systems (2010) 10. : Toward efficient full automatic operation of the PWR steam generator with water level adaptive control.

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