دانلود رایگان ترجمه مقاله تنوع رگرسیون چند متغیره برای تحلیل سیستم های مهندسی ژئوتکنیک – الزویر ۲۰۱۳
دانلود رایگان مقاله انگلیسی اسپلاین رگرسیون انطباقی چند متغیره برای تحلیل سیستم های مهندسی ژئوتکنیک به همراه ترجمه فارسی
عنوان فارسی مقاله: | اسپلاین رگرسیون انطباقی چند متغیره برای تحلیل سیستم های مهندسی ژئوتکنیک |
عنوان انگلیسی مقاله: | Multivariate adaptive regression splines for analysis of geotechnical engineering systems |
رشته های مرتبط: | مهندسی کامپیوتر و مهندسی عمران، خاک و پی یا ژئوتکنیک، هوش مصنوعی، مهندسی الگوریتم ها و محاسبات |
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نشریه | الزویر – Elsevier |
کد محصول | f298 |
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بخشی از ترجمه فارسی مقاله: ۲- جزئیات MARS ۴- تحلیل شبکه عصبی و سنجه های عملکرد ۵- تحلیل ها با استفاده از MARS |
بخشی از مقاله انگلیسی: ۲٫ Details of MARS MARS is a nonlinear and nonparametric regression method that models the nonlinear responses between the inputs and the output of a system by a series of piecewise linear segments (splines) of differing gradients. No specific assumption about the underlying functional relationship between the input variables and the output is required. The end points of the segments are called knots. A knot marks the end of one region of data and the beginning of another. The resulting piecewise curves (known as basis functions), give greater flexibility to the model, allowing for bends, thresholds, and other departures from linear functions. MARS generates basis functions by searching in a stepwise manner. An adaptive regression algorithm is used for selecting the knot locations. MARS models are constructed in a two-phase procedure. The forward phase adds functions and finds potential knots to improve the performance, resulting in an overfit model. The backward phase involves pruning the least effective terms. An open source code on MARS from Jekabsons [10] is used in carrying out the analyses presented in this paper. ۴٫ Neural network analysis and performance measures In the six geotechnical examples analyzed using MARS in the next section, the same data were also analyzed using a Matlabbased back-propagation algorithm BPNN for comparative purposes. For simplicity, these BPNN models are assumed to have a single hidden layer. The optimal BPNN architecture is obtained through a trial and error procedure, by varying the number of hidden neurons and the transfer function type (logsigmoid, tansigmoid, or purelin). Table 1 shows the various performance measures used to compare the predictions of the two metahueristic methods. In addition, the processing speed (CPU time) for both methods are also presented. For the final example (seismic liquefaction assessment) in which the dependent variable is not a continuous parameter but rather a binary event, a common measure of evaluating the performance of a pattern-classification model is to determine the success rate SR (the percentage of correctly classified cases). ۵٫ Analyses using MARS Nine examples are presented to illustrate the application and accuracy of MARS. Firstly, three examples consisting of fairly complicated mathematical functions (with single or two variables) are presented to demonstrate the function approximating capacity of MARS. This is followed by an example to evaluate the MARS effi- ciency in analyzing a hypothetical nonlinear function in which noise (error) is introduced. The last six are practical geotechnical examples that highlight the capability of MARS in modeling nonlinear multivariate problems.
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