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17 Apr 2017: tj), for example,. ... EDCC(tj-yj)2. (6)EDis expected to Increase if important input variableshave been excluded from the analysis.

23 Apr 2021: Dark etching regions under rolling contact fatigue: a review. S. Echeverri Restrepoa,b, S. W. Ooic, P. Yana, P. Andrica, R. H. Vegtera, and J. Laia. aSKF Research & Technology Development (RTD), SKF B.V., Meidoornkade 14, 3992 AE,Houten, The

14 Oct 2012: equations, of which equation j has tj coefficients. The horizontal lines divide the groups of.

22 Feb 2011: 18. Nandan R, Roy GG, Lienert TJ, DebRoy T. Acta Mater 2007;55:883. ... 49. Nandan R, Lienert TJ, DebRoy T. Int J Mater Res 2008;99:434.

7 Jun 2010: The noise in the output can be assessed by comparing thepredicted values (yj ) of the output against those measured(tj ), for example,. ... ED. j. (tj yj )2. (4). Fig. 3 Variations in the test and training errors as a function of model complexity, for

7 Jun 2010: COMPLEX MECHANICAL. PROPERTIES OF STEEL. Radu Calin Dimitriu. Department of Materials Science and Metallurgy. University of Cambridge. Churchill College. A dissertation submitted for thedegree of Doctor of Philosophy. at the University of

7 Jun 2010: 2 and 4). Thenoise in the output can be assessed by comparing thepredicted values yj of the output using this well fittednetwork, against those measured tj, for example,. ... ED!X. j. tj{yj 2. (1). ED should be expected to increase if important

7 Jun 2010: E =. j. (tj yj)2 (3). where yj is a predicted value and tj the target value; to calculate this error we normalised the outputto be in the range 0.5.

7 Jun 2010: The overall error in the neural network model, ED, is calculated by comparing the predicted values yj of the output against those measured tj!

7 Jun 2010: tj The measured value. xj Input variables in neural networks. wi Weights in neural networks. ... predicted values yj of the output against those measured value tj:.