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the differences in the forecast generated by the combined set (containing total of 100 examples) and the forecast generated by 50 ef cient and 50 inef cient cases Based on the results plotted in Figure 312, it can be seen that the difference between the combined forecast and the forecast generated by learning from ef cient cases is lower (RMS error 00245) than the difference between the combined forecast and the inef cient forecast (RMS 00667) This shows that for larger data sets, ANNs have a tendency to learn patterns from ef cient cases Inef cient cases in the training set lead to poor forecasting accuracy, hurt the monotonicity assumption, and foster inconsistent learning Among the key issues in the DEA assessment of technical ef ciency are: 1 Comparison of sets of DMUs that perform uniform operations 2 Assumption that, for ef cient DMUs, higher values of input should lead to higher values of output 3 No assumption made concerning the functional form that links the inputs and outputs The following are similarities between ANNs and DEA models: 1 DEA and ANNs are nonparametric models 2 Neither DEA nor ANNs make assumptions about the functional form that links its inputs to outputs 3 DEA seeks a set of weights to maximize the technical ef ciency, whereas ANNs seek a set of weights to derive the best possible t through observations of the training data set In the case of using DEA for preprocessing data for ANNs, DEA eliminates some of the inconsistencies in the data that may hurt the monotonicity assumption For example, if the training data set consists of two examples with the same inputs but different outputs, the example with a lower output receives a lower technical ef ciency The lower technical ef ciency increases the likelihood of elimination of the case with the lower ef ciency For a slight difference in outputs, the difference in ef ciency between the two examples may be low and both examples may be included in the training data However, for large differences in outputs, the case with a lower ef ciency may clearly be considered as an outlier (as this may hurt the monotonicity assumption) and has a higher likelihood of elimination from the ef cient training data Whether or not the low ef ciency case is nally eliminated from the ef cient data set depends on its actual ef ciency, number of total examples, and number of inputs, among other things The possibility of eliminating a low ef ciency case the from training data set is important when two examples show inconsistency; keeping both examples in the raining data set would impair the ANN learning Because ANN learning is through nonparametric regression-type learning of a nonlinear curve, outliers and inconsistent data impair its learning DEA models, on the.

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Using Barcode generator for NET Control to generate, create Code128 image in NET framework applications 128 In NET Framework Using Barcode reader for Microwave and radio- frequency heating, in Advances in Thermal and Non hermal Food Preservation (eds G Tewari, V Juneja), Blackwell Publishing, Ames, IA, pp 91 98 40 Vicente, A, Castro, IA (2006) Novel thermal processing technologies, in Advances in Thermal and Non-Thermal Food Preservation (eds G Tewari, V Juneja), Blackwell Publishing, Ames, IA, pp 99 130 41 Ohlsson, T, Bengtsson, N (2002b) Minimal processing of foods with thermal methods, in Minimal Processing Technologies in the Food Industry (eds T Ohlsson, N Bengtsson), Woodhead Publishing, Cambridge, pp 4 33 42 McKenna, BM, Lyng, J, Brunton, N, Shirsat, N (2006) Advances in radio frequency and ohmic heating of meats Journal of Food Engineering, 77, 215 229 43 Eliot-God reaux, S.

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, Zuber, F, Goullieux, A (2001) Processing and stabilisation of cauli ower by ohmic heating technology Innovative Food Science and Emerging Technologies, 2, 279 287 44 Salengke, S, Sastry, SK (2007) Models for ohmic heating of solid liquid mixtures under worst-case heating scenarios Journal of Food Engineering, 83, 337 355 45 Sandu, C (1986) Infrared radiative drying in food engineering: a process analysis Biotechnology Progress, 2, 109 119 46 Hashimoto, A, Sawai, J, Igarashi, H, Shimizu, M (1991) Effect of far-infrared radiation on pasteurization of bacteria suspended in phosphate-buffered saline Kagaku Kogaku Ronbunshu, 17, 627 633 47 Hamanaka, D, Dokan, S, Yasunaga, E, Kuroki, S, Uchino, T, Akimoto, K (2000) The sterilization effects on infrared ray of the agricultural products spoilage microorganisms (part 1) An ASAE Meeting Presentation, Milwaukee, WI, July 9 12, No 00 6090 48 Krishnamurthy, K.

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