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By Paraskevas D.Tzanavaras & Constantinos K. Zacharis
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Møller MF. A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks, 1993; 6: 525-533. Shapiro AF. The merging of neural networks, fuzzy logic, and genetic algorithms. Insur Math Econ 2002; 31: 115131. Di Silvestre1, S. Daminelli1, P. Brunetti1 and PL. it Abstract: In recent years mass spectrometry-based proteomics became very important and now it is the leading approach employed in high-throughput analysis. Its relevance increased thanks to availability of genome-sequence database and the development of high sensitivity instruments allows a rapid and automated proteins profiling.
RESULTS AND DISCUSSION ANN systems were simulated using Matlab Neural Network Toolbox running on a Pentium IV platform. Training continued until there was no further decrease in validation error. e training, validate or test set) and outij is the element of the output matrix out ( N g ) of the neural network. The data set was divided in the unbiased way described above, into a training set of 80 data, a validation set and a test set of 20 data each; the training data set was used for model building, while the validation data set was utilized for model validation.
Neural networks: a review from a statistical perspective (with discussion). Stat Sci 1994; 9: 2-54. Warner B, Misra M. Understanding Neural Networks as Statistical Tools. Am Stat 1996: 50: 284-293. , Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J Clin Epidemiol 1996; 49: 1225-1231. Mjalli FS, Al-Asheh1 S, Alfadalaa HE. Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance.