دانلود مقاله ISI انگلیسی شماره 52497
عنوان فارسی مقاله

شبکه های عصبی مصنوعی یک سیگنال آب و هوا با وضوح بالا در سیما شناسی برگ را نشان می دهد

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
52497 2016 11 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Artificial neural networks reveal a high-resolution climatic signal in leaf physiognomy
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Palaeogeography, Palaeoclimatology, Palaeoecology, Volume 442, 15 January 2016, Pages 1–11

کلمات کلیدی
شبکه های عصبی مصنوعی - آب و هوا - فسیلی - سیما شناسی برگ
پیش نمایش مقاله
پیش نمایش مقاله شبکه های عصبی مصنوعی یک سیگنال آب و هوا با وضوح بالا در سیما شناسی برگ را نشان می دهد

چکیده انگلیسی

The relationship linking leaf physiognomy and climate has long been used in paleoclimatic reconstructions, but current models lose precision when worldwide data sets are considered because of the broader range of physiognomies that occur under the wider range of climate types represented. Our aim is to improve the predictive power of leaf physiognomy to yield climate signals, and here we explore the use of an algorithm based on the general regression neural network (GRNN), which we refer to as Climate Leaf Analysis with Neural Networks (CLANN). We then test our algorithm on Climate Leaf Analysis Multivariate Program (CLAMP) data sets and digital leaf physiognomy (DLP) data sets, and compare our results with those obtained from other computation methods. We explore the contribution of different physiognomic characters and test fossil sites from North America. The CLANN algorithm introduced here gives high predictive precision for all tested climatic parameters in both data sets. For the CLAMP data set neural network analysis improves the predictive capability as measured by R2, to 0.86 for MAT on a worldwide basis, compared to 0.71 using the vector-based approach used in the standard analysis. Such a high resolution is attained due to the nonlinearity of the method, but at the cost of being susceptible to ‘noise’ in the calibration data. Tests show that the predictions are repeatable, and robust to information loss and applicable to fossil leaf data. The CLANN neural network algorithm used here confirms, and better resolves, the global leaf form–climate relationship, opening new approaches to paleoclimatic reconstruction and understanding the evolution of complex leaf function.

خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.