Advances in Computational Intelligence by Prateek Agrawal, Anupam Shukla, Ritu Tiwari (auth.), Wen Yu, PDF

By Prateek Agrawal, Anupam Shukla, Ritu Tiwari (auth.), Wen Yu, Edgar N. Sanchez (eds.)

ISBN-10: 3642031552

ISBN-13: 9783642031557

ISBN-10: 3642031560

ISBN-13: 9783642031564

This booklet constitutes the court cases of the second one overseas Workshop on complicated Computational Intelligence (IWACI2009), with a sequel of IWACI 2008 effectively held in Macao, China. IWACI2009 supplied a high-level foreign discussion board for scientists, engineers, and educators to provide cutting-edge learn in computational intelligence and comparable fields.

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2008, pp. 168–178 (2008) 15. : Preprocessing Data for Neural Networks. 2008) 16. : Introduction to Neural Networks for Java, 2nd edn. Heaton Research (October 2008) 17. : Introduction, Association Rules Mining: A Recent Overview, Educational Software Development Laboratory, Department of Mathematics, University of Patras, Greece (2006) 18. : Prediction of Tides using back-Propergation Neural Networks. In: International Conference in Ocean Engineering (December 2001) 19. : A Self Growing Cluster Development Approach to Data Mining.

5. Prediction of temperature of motor radial bearing Fig. 6. Prediction of temperature of motor thrust bearing 41 42 G. Yan and Y. Zhu 5 Conclusions Condition predicting is very important for predictive maintenance. In order to apply SVMs into real practice effectively, we determine the parameters directly from analysing train data without running SVM training process. This method not only guarantees the predicting accuracy, but also reduces the consumed time into the minimum. Combining the phase space reconstruction, Local SVMs are used in the trend prediction of field data of reactor coolant pump.

If C is too small (zero), then the objective does not consider the penalty on deviations which is larger than ε . Under these two situations, it is impossible for SVMs to obtain good regularization performance. A good value of C should be chosen according to the range of output values of training data [9,10]. The prescription for parameter C is given by: C = max( y + 3σ y , y − 3σ y ) (7) where y and σ y are the mean value and the standard deviation of training targets respectively. Selection of parameter λ .

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Advances in Computational Intelligence by Prateek Agrawal, Anupam Shukla, Ritu Tiwari (auth.), Wen Yu, Edgar N. Sanchez (eds.)

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